Development of an enzyme immobilization platform based on microencapsulation for paper-based biosensors

Un papier bioactif est obtenu par la modification d’un papier en y immobilisant une ou plusieurs biomolécules. La recherche et le développement de papiers bioactifs est en plein essor car le papier est un substrat peu dispendieux qui est déjà d’usage très répandu à travers le monde. Bien que les papiers bioactifs n’aient pas connus de succès commercial depuis la mise en marche de bandelettes mesurant le taux de glucose dans les années cinquante, de nombreux groupes de recherche travaillent à immobiliser des biomolécules sur le papier pour obtenir un papier bioactif qui est abordable et possède une bonne durée de vie. Contrairement à la glucose oxidase, l’enzyme utilisée sur ces bandelettes, la majorité des biomolécules sont très fragiles et perdent leur activité très rapidement lorsqu’immobilisées sur des papiers. Le développement de nouveaux papiers bioactifs pouvant détecter des substances d’intérêt ou même désactiver des pathogènes dépend donc de découverte de nouvelles techniques d’immobilisation des biomolécules permettant de maintenir leur activité tout en étant applicable dans la chaîne de production actuelle des papiers fins. Le but de cette thèse est de développer une technique d’immobilisation efficace et versatile, permettant de protéger l’activité de biomolécules incorporées sur des papiers. La microencapsulation a été choisie comme technique d’immobilisation car elle permet d’enfermer de grandes quantités de biomolécules à l’intérieur d’une sphère poreuse permettant leur protection. Pour cette étude, le polymère poly(éthylènediimine) a été choisi afin de générer la paroi des microcapsules. Les enzymes laccase et glucose oxidase, dont les propriétés sont bien établies, seront utilisées comme biomolécules test. Dans un premier temps, deux procédures d’encapsulation ont été développées puis étudiées. La méthode par émulsion produit des microcapsules de plus petits diamètres que la méthode par encapsulation utilisant un encapsulateur, bien que cette dernière offre une meilleure efficacité d’encapsulation. Par la suite, l’effet de la procédure d’encapsulation sur l’activité enzymatique et la stabilité thermique des enzymes a été étudié à cause de l’importance du maintien de l’activité sur le développement d’une plateforme d’immobilisation. L’effet de la nature du polymère utilisé pour la fabrication des capsules sur la conformation de l’enzyme a été étudié pour la première fois. Finalement, l’applicabilité des microcapsules de poly(éthylèneimine) dans la confection de papiers bioactifs a été démontré par le biais de trois prototypes. Un papier réagissant au glucose a été obtenu en immobilisant des microcapsules contenant l’enzyme glucose oxidase. Un papier sensible à l’enzyme neuraminidase pour la détection de la vaginose bactérienne avec une plus grande stabilité durant l’entreposage a été fait en encapsulant les réactifs colorimétriques dans des capsules de poly(éthylèneimine). L’utilisation de microcapsules pour l’immobilisation d’anticorps a également été étudiée. Les avancées au niveau de la plateforme d’immobilisation de biomolécules par microencapsulation qui ont été réalisées lors de cette thèse permettront de mieux comprendre l’effet des réactifs impliqués dans la procédure de microencapsulation sur la stabilité, l’activité et la conformation des biomolécules. Les résultats obtenus démontrent que la plateforme d’immobilisation développée peut être appliquée pour la confection de nouveaux papiers bioactifs.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.

A Diagnostic and Modeling Study on the Onset of Summer Monsoon over South Asia

This study focuses on the onset of southwest monsoon over Kerala. India Meteorological Department (IMD) has been using a semi-objective method to define monsoon onset. The main objectives of the study are to understand the monsoon onset processes, to simulate monsoon onset in a GCM using as input the atmospheric conditions and Sea Surface Temperature, 10 days earlier to the onset, to develop a method for medium range prediction of the date of onset of southwest monsoon over Kerala and to examine the possibility of objectively defining the date of Monsoon Onset over Kerala (MOK). It gives a broad description of regional monsoon systems and monsoon onsets over Asia and Australia. Asian monsoon includes two separate subsystems, Indain monsoon and East Asian monsoon. It is seen from this study that the duration of the different phases of the onset process are dependent on the period of ISO. Based on the study of the monsoon onset process, modeling studies can be done for better understanding of the ocean-atmosphere interaction especially those associated with the warm pool in the Bay of Bengal and the Arabian Sea.

Modeling of Suspended Sediment Dynamics in Tropical River Basins

It is proposed to study the suspended sediment transport characteristics of river basins of Kerala and to model suspended sediment discharge mechanism for typical micro-watersheds. The Pamba river basin is selected as a representative hydrologic regime for detailed studies of suspended sediment characteristics and its seasonal variation. The applicability of various erosion models would be tested by comparing with the observed event data (by continuous monitoring of rainfall, discharge, and suspended sediment concentration for lower order streams). Empirical, conceptual and physically distributed models were used for making the comparison of performance of the models. Large variations in the discharge and sediment quantities were noticed during a particular year between the river basins investigated and for an individual river basin during the years for which the data was available. In general, the sediment yield pattern follows the seasonal distribution of rainfall, discharge and physiography of the land. This confirms with similar studies made for other Indian rivers. It was observed from this study, that the quantity of sediment transported downstream shows a decreasing trend over the years corresponding to increase in discharge. For sound and sustainable management of coastal zones, it is important to understand the balance between erosion and retention and to quantify the exact amount of the sediments reaching this eco-system. This, of course, necessitates a good length of time series data and more focused research on the behaviour of each river system, both present and past. In this realm of river inputs to ocean system, each of the 41 rivers of Kerala may have dominant yet diversified roles to influence the coastal ecosystem as reflected from this study on the major fraction of transport, namely the suspended sediments

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Fractal based techniques for classification of mammograms and identification of microcalcifications

After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of • A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant) • A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram. The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.

Design and Development of a Frame Based MT System for English-to-ISL

This paper presents the design and development of a frame based approach for speech to sign language machine translation system in the domain of railways and banking. This work aims to utilize the capability of Artificial intelligence for the improvement of physically challenged, deaf-mute people. Our work concentrates on the sign language used by the deaf community of Indian subcontinent which is called Indian Sign Language (ISL). Input to the system is the clerk’s speech and the output of this system is a 3D virtual human character playing the signs for the uttered phrases. The system builds up 3D animation from pre-recorded motion capture data. Our work proposes to build a Malayalam to ISL

Quantile based entropy function

Quantile functions are efficient and equivalent alternatives to distribution functions in modeling and analysis of statistical data (see Gilchrist, 2000; Nair and Sankaran, 2009). Motivated by this, in the present paper, we introduce a quantile based Shannon entropy function. We also introduce residual entropy function in the quantile setup and study its properties. Unlike the residual entropy function due to Ebrahimi (1996), the residual quantile entropy function determines the quantile density function uniquely through a simple relationship. The measure is used to define two nonparametric classes of distributions

Quantile based reliability aspects of partial moments

Partial moments are extensively used in literature for modeling and analysis of lifetime data. In this paper, we study properties of partial moments using quantile functions. The quantile based measure determines the underlying distribution uniquely. We then characterize certain lifetime quantile function models. The proposed measure provides alternate definitions for ageing criteria. Finally, we explore the utility of the measure to compare the characteristics of two lifetime distributions

Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.

A New Fast Fractal Modeling Approach for the Detection of Microcalcifications in Mammograms

In this paper, a novel fast method for modeling mammograms by deterministic fractal coding approach to detect the presence of microcalcifications, which are early signs of breast cancer, is presented. The modeled mammogram obtained using fractal encoding method is visually similar to the original image containing microcalcifications, and therefore, when it is taken out from the original mammogram, the presence of microcalcifications can be enhanced. The limitation of fractal image modeling is the tremendous time required for encoding. In the present work, instead of searching for a matching domain in the entire domain pool of the image, three methods based on mean and variance, dynamic range of the image blocks, and mass center features are used. This reduced the encoding time by a factor of 3, 89, and 13, respectively, in the three methods with respect to the conventional fractal image coding method with quad tree partitioning. The mammograms obtained from The Mammographic Image Analysis Society database (ground truth available) gave a total detection score of 87.6%, 87.6%, 90.5%, and 87.6%, for the conventional and the proposed three methods, respectively.

Reliability Estimation of Open Source Software based Computational Systems

Software systems are progressively being deployed in many facets of human life. The implication of the failure of such systems, has an assorted impact on its customers. The fundamental aspect that supports a software system, is focus on quality. Reliability describes the ability of the system to function under specified environment for a specified period of time and is used to objectively measure the quality. Evaluation of reliability of a computing system involves computation of hardware and software reliability. Most of the earlier works were given focus on software reliability with no consideration for hardware parts or vice versa. However, a complete estimation of reliability of a computing system requires these two elements to be considered together, and thus demands a combined approach. The present work focuses on this and presents a model for evaluating the reliability of a computing system. The method involves identifying the failure data for hardware components, software components and building a model based on it, to predict the reliability. To develop such a model, focus is given to the systems based on Open Source Software, since there is an increasing trend towards its use and only a few studies were reported on the modeling and measurement of the reliability of such products. The present work includes a thorough study on the role of Free and Open Source Software, evaluation of reliability growth models, and is trying to present an integrated model for the prediction of reliability of a computational system. The developed model has been compared with existing models and its usefulness of is being discussed.

Air-gap based vertical cavity micro-opto-electro-mechanical Fabry-Perot filters

Mikrooptische Filter sind heutzutage in vielen Bereichen in der Telekommunikation unersetzlich. Wichtige Einsatzgebiete sind aber auch spektroskopische Systeme in der Medizin-, Prozess- und Umwelttechnik. Diese Arbeit befasst sich mit der Technologieentwicklung und Herstellung von luftspaltbasierenden, vertikal auf einem Substrat angeordneten, oberflächenmikromechanisch hergestellten Fabry-Perot-Filtern. Es werden zwei verschiedene Filtervarianten, basierend auf zwei verschiedenen Materialsystemen, ausführlich untersucht. Zum einen handelt es sich dabei um die Weiterentwicklung von kontinuierlich mikromechanisch durchstimmbaren InP / Luftspaltfiltern; zum anderen werden neuartige, kostengünstige Siliziumnitrid / Luftspaltfilter wissenschaftlich behandelt. Der Inhalt der Arbeit ist so gegliedert, dass nach einer Einleitung mit Vergleichen zu Arbeiten und Ergebnissen anderer Forschergruppen weltweit, zunächst einige theoretische Grundlagen zur Berechnung der spektralen Reflektivität und Transmission von beliebigen optischen Schichtanordnungen aufgezeigt werden. Auß erdem wird ein kurzer theoretischer Ü berblick zu wichtigen Eigenschaften von Fabry-Perot-Filtern sowie der Möglichkeit einer mikromechanischen Durchstimmbarkeit gegeben. Daran anschließ end folgt ein Kapitel, welches sich den grundlegenden technologischen Aspekten der Herstellung von luftspaltbasierenden Filtern widmet. Es wird ein Zusammenhang zu wichtigen Referenzarbeiten hergestellt, auf denen diverse Weiterentwicklungen dieser Arbeit basieren. Die beiden folgenden Kapitel erläutern dann ausführlich das Design, die Herstellung und die Charakterisierung der beiden oben erwähnten Filtervarianten. Abgesehen von der vorangehenden Epitaxie von InP / GaInAs Schichten, ist die Herstellung der InP / Luftspaltfilter komplett im Institut durchgeführt worden. Die Herstellungsschritte sind ausführlich in der Arbeit erläutert, wobei ein Schwerpunktthema das trockenchemische Ä tzen von InP sowie GaInAs, welches als Opferschichtmaterial für die Herstellung der Luftspalte genutzt wurde, behandelt. Im Verlauf der wissenschaftlichen Arbeit konnten sehr wichtige technische Verbesserungen entwickelt und eingesetzt werden, welche zu einer effizienteren technologischen Herstellung der Filter führten und in der vorliegenden Niederschrift ausführlich dokumentiert sind. Die hergestellten, für einen Einsatz in der optischen Telekommunikation entworfenen, elektrostatisch aktuierbaren Filter sind aus zwei luftspaltbasierenden Braggspiegeln aufgebaut, welche wiederum jeweils 3 InP-Schichten von (je nach Design) 357nm bzw. 367nm Dicke aufweisen. Die Filter bestehen aus im definierten Abstand parallel übereinander angeordneten Membranen, die über Verbindungsbrücken unterschiedlicher Anzahl und Länge an Haltepfosten befestigt sind. Da die mit 357nm bzw. 367nm vergleichsweise sehr dünnen Schichten freitragende Konstrukte mit bis zu 140 nm Länge bilden, aber trotzdem Positionsgenauigkeiten im nm-Bereich einhalten müssen, handelt es sich hierbei um sehr anspruchsvolle mikromechanische Bauelemente. Um den Einfluss der zahlreichen geometrischen Strukturparameter studieren zu können, wurden verschiedene laterale Filterdesigns implementiert. Mit den realisierten Filter konnte ein enorm weiter spektraler Abstimmbereich erzielt werden. Je nach lateralem Design wurden internationale Bestwerte für durchstimmbare Fabry-Perot-Filter von mehr als 140nm erreicht. Die Abstimmung konnte dabei kontinuierlich mit einer angelegten Spannung von nur wenigen Volt durchgeführt werden. Im Vergleich zu früher berichteten Ergebnissen konnten damit sowohl die Wellenlängenabstimmung als auch die dafür benötigte Abstimmungsspannung signifikant verbessert werden. Durch den hohen Brechungsindexkontrast und die geringe Schichtdicke zeigen die Filter ein vorteilhaftes, extrem weites Stopband in der Größ enordnung um 550nm. Die gewählten, sehr kurzen Kavitätslängen ermöglichen einen freien Spektralbereich des Filters welcher ebenfalls in diesen Größ enordnungen liegt, so dass ein weiter spektraler Einsatzbereich ermöglicht wird. Während der Arbeit zeigte sich, dass Verspannungen in den freitragenden InPSchichten die Funktionsweise der mikrooptischen Filter stark beeinflussen bzw. behindern. Insbesondere eine Unterätzung der Haltepfosten und die daraus resultierende Verbiegung der Ecken an denen sich die Verbindungsbrücken befinden, führte zu enormen vertikalen Membranverschiebungen, welche die Filtereigenschaften verändern. Um optimale Ergebnisse zu erreichen, muss eine weitere Verbesserung der Epitaxie erfolgen. Jedoch konnten durch den zusätzlichen Einsatz einer speziellen Schutzmaske die Unterätzung der Haltepfosten und damit starke vertikale Verformungen reduziert werden. Die aus der Verspannung resultierenden Verformungen und die Reaktion einzelner freistehender InP Schichten auf eine angelegte Gleich- oder Wechselspannung wurde detailliert untersucht. Mittels Weisslichtinterferometrie wurden lateral identische Strukturen verglichen, die aus unterschiedlich dicken InP-Schichten (357nm bzw. 1065nm) bestehen. Einen weiteren Hauptteil der Arbeit stellen Siliziumnitrid / Luftspaltfilter dar, welche auf einem neuen, im Rahmen dieser Dissertation entwickelten, technologischen Ansatz basieren. Die Filter bestehen aus zwei Braggspiegeln, die jeweils aus fünf 590nm dicken, freistehenden Siliziumnitridschichten aufgebaut sind und einem Abstand von 390nm untereinander aufweisen. Die Filter wurden auf Glassubstraten hergestellt. Der Herstellungsprozess ist jedoch auch mit vielen anderen Materialien oder Prozessen kompatibel, so dass z.B. eine Integration mit anderen Bauelemente relativ leicht möglich ist. Die Prozesse dieser ebenfalls oberflächenmikromechanisch hergestellten Filter wurden konsequent auf niedrige Herstellungskosten optimiert. Als Opferschichtmaterial wurde hier amorph abgeschiedenes Silizium verwendet. Der Herstellungsprozess beinhaltet die Abscheidung verspannungsoptimierter Schichten (Silizium und Siliziumnitrid) mittels PECVD, die laterale Strukturierung per reaktiven Ionenätzen mit den Gasen SF6 / CHF3 / Ar sowie Fotolack als Maske, die nasschemische Unterätzung der Opferschichten mittels KOH und das Kritisch-Punkt-Trocken der Proben. Die Ergebnisse der optischen Charakterisierung der Filter zeigen eine hohe Ü bereinstimmung zwischen den experimentell ermittelten Daten und den korrespondierenden theoretischen Modellrechnungen. Weisslichtinterferometermessungen der freigeätzten Strukturen zeigen ebene Filterschichten und bestätigen die hohe vertikale Positioniergenauigkeit, die mit diesem technologischen Ansatz erreicht werden kann.

A Modeling Analysis of Changing Global Land Use as affected by Changing Demands for Wood Products

The 21st century has brought new challenges for forest management at a time when globalization in world trade is increasing and global climate change is becoming increasingly apparent. In addition to various goods and services like food, feed, timber or biofuels being provided to humans, forest ecosystems are a large store of terrestrial carbon and account for a major part of the carbon exchange between the atmosphere and the land surface. Depending on the stage of the ecosystems and/or management regimes, forests can be either sinks, or sources of carbon. At the global scale, rapid economic development and a growing world population have raised much concern over the use of natural resources, especially forest resources. The challenging question is how can the global demands for forest commodities be satisfied in an increasingly globalised economy, and where could they potentially be produced? For this purpose, wood demand estimates need to be integrated in a framework, which is able to adequately handle the competition for land between major land-use options such as residential land or agricultural land. This thesis is organised in accordance with the requirements to integrate the simulation of forest changes based on wood extraction in an existing framework for global land-use modelling called LandSHIFT. Accordingly, the following neuralgic points for research have been identified: (1) a review of existing global-scale economic forest sector models (2) simulation of global wood production under selected scenarios (3) simulation of global vegetation carbon yields and (4) the implementation of a land-use allocation procedure to simulate the impact of wood extraction on forest land-cover. Modelling the spatial dynamics of forests on the global scale requires two important inputs: (1) simulated long-term wood demand data to determine future roundwood harvests in each country and (2) the changes in the spatial distribution of woody biomass stocks to determine how much of the resource is available to satisfy the simulated wood demands. First, three global timber market models are reviewed and compared in order to select a suitable economic model to generate wood demand scenario data for the forest sector in LandSHIFT. The comparison indicates that the ‘Global Forest Products Model’ (GFPM) is most suitable for obtaining projections on future roundwood harvests for further study with the LandSHIFT forest sector. Accordingly, the GFPM is adapted and applied to simulate wood demands for the global forestry sector conditional on selected scenarios from the Millennium Ecosystem Assessment and the Global Environmental Outlook until 2050. Secondly, the Lund-Potsdam-Jena (LPJ) dynamic global vegetation model is utilized to simulate the change in potential vegetation carbon stocks for the forested locations in LandSHIFT. The LPJ data is used in collaboration with spatially explicit forest inventory data on aboveground biomass to allocate the demands for raw forest products and identify locations of deforestation. Using the previous results as an input, a methodology to simulate the spatial dynamics of forests based on wood extraction is developed within the LandSHIFT framework. The land-use allocation procedure specified in the module translates the country level demands for forest products into woody biomass requirements for forest areas, and allocates these on a five arc minute grid. In a first version, the model assumes only actual conditions through the entire study period and does not explicitly address forest age structure. Although the module is in a very preliminary stage of development, it already captures the effects of important drivers of land-use change like cropland and urban expansion. As a first plausibility test, the module performance is tested under three forest management scenarios. The module succeeds in responding to changing inputs in an expected and consistent manner. The entire methodology is applied in an exemplary scenario analysis for India. A couple of future research priorities need to be addressed, particularly the incorporation of plantation establishments; issue of age structure dynamics; as well as the implementation of a new technology change factor in the GFPM which can allow the specification of substituting raw wood products (especially fuelwood) by other non-wood products.

Modeling the interplay between land use, bioenergy production and climate change

Land use has become a force of global importance, considering that 34% of the Earth’s ice-free surface was covered by croplands or pastures in 2000. The expected increase in global human population together with eminent climate change and associated search for energy sources other than fossil fuels can, through land-use and land-cover changes (LUCC), increase the pressure on nature’s resources, further degrade ecosystem services, and disrupt other planetary systems of key importance to humanity. This thesis presents four modeling studies on the interplay between LUCC, increased production of biofuels and climate change in four selected world regions. In the first study case two new crop types (sugarcane and jatropha) are parameterized in the LPJ for managed Lands dynamic global vegetation model for calculation of their potential productivity. Country-wide spatial variation in the yields of sugarcane and jatropha incurs into substantially different land requirements to meet the biofuel production targets for 2015 in Brazil and India, depending on the location of plantations. Particularly the average land requirements for jatropha in India are considerably higher than previously estimated. These findings indicate that crop zoning is important to avoid excessive LUCC. In the second study case the LandSHIFT model of land-use and land-cover changes is combined with life cycle assessments to investigate the occurrence and extent of biofuel-driven indirect land-use changes (ILUC) in Brazil by 2020. The results show that Brazilian biofuels can indeed cause considerable ILUC, especially by pushing the rangeland frontier into the Amazonian forests. The carbon debt caused by such ILUC would result in no carbon savings (from using plant-based ethanol and biodiesel instead of fossil fuels) before 44 years for sugarcane ethanol and 246 years for soybean biodiesel. The intensification of livestock grazing could avoid such ILUC. We argue that such an intensification of livestock should be supported by the Brazilian biofuel sector, based on the sector’s own interest in minimizing carbon emissions. In the third study there is the development of a new method for crop allocation in LandSHIFT, as influenced by the occurrence and capacity of specific infrastructure units. The method is exemplarily applied in a first assessment of the potential availability of land for biogas production in Germany. The results indicate that Germany has enough land to fulfill virtually all (90 to 98%) its current biogas plant capacity with only cultivated feedstocks. Biogas plants located in South and Southwestern (North and Northeastern) Germany might face more (less) difficulties to fulfill their capacities with cultivated feedstocks, considering that feedstock transport distance to plants is a crucial issue for biogas production. In the fourth study an adapted version of LandSHIFT is used to assess the impacts of contrasting scenarios of climate change and conservation targets on land use in the Brazilian Amazon. Model results show that severe climate change in some regions by 2050 can shift the deforestation frontier to areas that would experience low levels of human intervention under mild climate change (such as the western Amazon forests or parts of the Cerrado savannas). Halting deforestation of the Amazon and of the Brazilian Cerrado would require either a reduction in the production of meat or an intensification of livestock grazing in the region. Such findings point out the need for an integrated/multicisciplinary plan for adaptation to climate change in the Amazon. The overall conclusions of this thesis are that (i) biofuels must be analyzed and planned carefully in order to effectively reduce carbon emissions; (ii) climate change can have considerable impacts on the location and extent of LUCC; and (iii) intensification of grazing livestock represents a promising venue for minimizing the impacts of future land-use and land-cover changes in Brazil.