En écologie, dans le cadre par exemple d’études des services fournis par les écosystèmes, les modélisations descriptive, explicative et prédictive ont toutes trois leur place distincte. Certaines situations bien précises requièrent soit l’un soit l’autre de ces types de modélisation ; le bon choix s’impose afin de pouvoir faire du modèle un usage conforme aux objectifs de l’étude. Dans le cadre de ce travail, nous explorons dans un premier temps le pouvoir explicatif de l’arbre de régression multivariable (ARM). Cette méthode de modélisation est basée sur un algorithme récursif de bipartition et une méthode de rééchantillonage permettant l’élagage du modèle final, qui est un arbre, afin d’obtenir le modèle produisant les meilleures prédictions. Cette analyse asymétrique à deux tableaux permet l’obtention de groupes homogènes d’objets du tableau réponse, les divisions entre les groupes correspondant à des points de coupure des variables du tableau explicatif marquant les changements les plus abrupts de la réponse. Nous démontrons qu’afin de calculer le pouvoir explicatif de l’ARM, on doit définir un coefficient de détermination ajusté dans lequel les degrés de liberté du modèle sont estimés à l’aide d’un algorithme. Cette estimation du coefficient de détermination de la population est pratiquement non biaisée. Puisque l’ARM sous-tend des prémisses de discontinuité alors que l’analyse canonique de redondance (ACR) modélise des gradients linéaires continus, la comparaison de leur pouvoir explicatif respectif permet entre autres de distinguer quel type de patron la réponse suit en fonction des variables explicatives. La comparaison du pouvoir explicatif entre l’ACR et l’ARM a été motivée par l’utilisation extensive de l’ACR afin d’étudier la diversité bêta. Toujours dans une optique explicative, nous définissons une nouvelle procédure appelée l’arbre de régression multivariable en cascade (ARMC) qui permet de construire un modèle tout en imposant un ordre hiérarchique aux hypothèses à l’étude. Cette nouvelle procédure permet d’entreprendre l’étude de l’effet hiérarchisé de deux jeux de variables explicatives, principal et subordonné, puis de calculer leur pouvoir explicatif. L’interprétation du modèle final se fait comme dans une MANOVA hiérarchique. On peut trouver dans les résultats de cette analyse des informations supplémentaires quant aux liens qui existent entre la réponse et les variables explicatives, par exemple des interactions entres les deux jeux explicatifs qui n’étaient pas mises en évidence par l’analyse ARM usuelle. D’autre part, on étudie le pouvoir prédictif des modèles linéaires généralisés en modélisant la biomasse de différentes espèces d’arbre tropicaux en fonction de certaines de leurs mesures allométriques. Plus particulièrement, nous examinons la capacité des structures d’erreur gaussienne et gamma à fournir les prédictions les plus précises. Nous montrons que pour une espèce en particulier, le pouvoir prédictif d’un modèle faisant usage de la structure d’erreur gamma est supérieur. Cette étude s’insère dans un cadre pratique et se veut un exemple pour les gestionnaires voulant estimer précisément la capture du carbone par des plantations d’arbres tropicaux. Nos conclusions pourraient faire partie intégrante d’un programme de réduction des émissions de carbone par les changements d’utilisation des terres. / In ecology, in ecosystem services studies for example, descriptive, explanatory and predictive modelling all have relevance in different situations. Precise circumstances may require one or the other type of modelling; it is important to choose the method properly to insure that the final model fits the study’s goal. In this thesis, we first explore the explanatory power of the multivariate regression tree (MRT). This modelling technique is based on a recursive bipartitionning algorithm. The tree is fully grown by successive bipartitions and then it is pruned by resampling in order to reveal the tree providing the best predictions. This asymmetric analysis of two tables produces homogeneous groups in terms of the response that are constrained by splitting levels in the values of some of the most important explanatory variables. We show that to calculate the explanatory power of an MRT, an appropriate adjusted coefficient of determination must include an estimation of the degrees of freedom of the MRT model through an algorithm. This estimation of the population coefficient of determination is practically unbiased. Since MRT is based upon discontinuity premises whereas canonical redundancy analysis (RDA) models continuous linear gradients, the comparison of their explanatory powers enables one to distinguish between those two patterns of species distributions along the explanatory variables. The extensive use of RDA for the study of beta diversity motivated the comparison between its explanatory power and that of MRT. In an explanatory perspective again, we define a new procedure called a cascade of multivariate regression trees (CMRT). This procedure provides the possibility of computing an MRT model where an order is imposed to nested explanatory hypotheses. CMRT provides a framework to study the exclusive effect of a main and a subordinate set of explanatory variables by calculating their explanatory powers. The interpretation of the final model is done as in nested MANOVA. New information may arise from this analysis about the relationship between the response and the explanatory variables, for example interaction effects between the two explanatory data sets that were not evidenced by the usual MRT model. On the other hand, we study the predictive power of generalized linear models (GLM) to predict individual tropical tree biomass as a function of allometric shape variables. Particularly, we examine the capacity of gaussian and gamma error structures to provide the most precise predictions. We show that for a particular species, gamma error structure is superior in terms of predictive power. This study is part of a practical framework; it is meant to be used as a tool for managers who need to precisely estimate the amount of carbon recaptured by tropical tree plantations. Our conclusions could be integrated within a program of carbon emission reduction by land use changes.

This thesis is concerned with recursive partitioning of models of a generalized linear model type (GLM-type), i.e., maximum likelihood models with a linear predictor for the linked mean, a topic that has received constant interest over the last twenty years. The resulting tree (a ''model tree'') can be seen as an extension of classic trees, to allow for a GLM-type model in the partitions. In this work, the focus lies on applied and computational aspects of model trees with GLM-type node models to work out different areas where application of the combination of parametric models and trees will be beneficial and to build a computational scaffold for future application of model trees. In the first part, model trees are defined and some algorithms for fitting model trees with GLM-type node model are reviewed and compared in terms of their properties of tree induction and node model fitting. Additionally, the design of a particularly versatile algorithm, the MOB algorithm (Zeileis et al. 2008) in R is described and an in-depth discussion of how the functionality offered can be extended to various GLM-type models is provided. This is highlighted by an example of using partitioned negative binomial models for investigating the effect of health care incentives. Part 2 consists of three research articles where model trees are applied to different problems that frequently occur in the social sciences. The first uses trees with GLM-type node models and applies it to a data set of voters, who show a non-monotone relationship between the frequency of attending past elections and the turnout in 2004. Three different type of model tree algorithms are used to investigate this phenomenon and for two the resulting trees can explain the counter-intuitive finding. Here model tress are used to learn a nonlinear relationship between a target model and a big number of candidate variables to provide more insight into a data set. A second application area is also discussed, namely using model trees to detect ill-fitting subsets in the data. The second article uses model trees to model the number of fatalities in Afghanistan war, based on the WikiLeaks Afghanistan war diary. Data pre-processing with a topic model generates predictors that are used as explanatory variables in a model tree for overdispersed count data. Here the combination of model trees and topic models allows to flexibly analyse database data, frequently encountered in data journalism, and provides a coherent description of fatalities in the Afghanistan war. The third paper uses a new framework built around model trees to approach the classic problem of segmentation, frequently encountered in marketing and management science. Here, the framework is used for segmentation of a sample of the US electorate for identifying likely and unlikely voters. It is shown that the framework's model trees enable accurate identification which in turn allows efficient targeted mobilisation of eligible voters. (author's abstract)

Animal traits differ not only in mean, but also in variation around the mean. For instance, one sire’s daughter group may be very homogeneous, while another sire’s daughters are much more heterogeneous in performance. The difference in residual variance can partially be explained by genetic differences. Models for such genetic heterogeneity of environmental variance include genetic effects for the mean and residual variance, and a correlation between the genetic effects for the mean and residual variance to measure how the residual variance might vary with the mean. The aim of this thesis was to develop a method based on double hierarchical generalized linear models for estimating genetic heteroscedasticity, and to apply it on four traits in two domestic animal species; teat count and litter size in pigs, and milk production and somatic cell count in dairy cows. The method developed is fast and has been implemented in software that is widely used in animal breeding, which makes it convenient to use. It is based on an approximation of double hierarchical generalized linear models by normal distributions. When having repeated observations on individuals or genetic groups, the estimates were found to be unbiased. For the traits studied, the estimated heritability values for the mean and the residual variance, and the genetic coefficients of variation, were found in the usual ranges reported. The genetic correlation between mean and residual variance was estimated for the pig traits only, and was found to be favorable for litter size, but unfavorable for teat count.

Die vorliegende Studie untersucht die nächtliche Habitatnutzung von Feldhasen in drei unterschiedlichen Habitaten. Das Untersuchungsgebiet Opferbaum ist stark ackerbaulich geprägt und das Untersuchungsgebiet Güntersleben sehr strukturreich durch das Vorkommen von Gehölzen und Waldrändern. Das Untersuchungsgebiet Fritzlar besitzt einen waldrandgeprägten sowie einen ackerbaulich intensiv genutzten Landschaftsteil. Die nächtlichen Aufenthaltsorte von Feldhasen wurden mittels Wärmebildkamera zwischen September 2004 bis April 2005 und September 2005 bis April 2006 kartiert. In jedem der Untersuchungsgebiete wurden einmal monatlich sogenannte Festpunkte angefahren, die umliegenden Landschaftsbereiche abgesucht und beobachtete Feldhasen in Arbeitskarten eingezeichnet. Eine Kartierung der von den Festpunkten einsehbaren Landschaftsteile geschah vor jedem Erfassungstermin bei Tageslicht. Den kartierten Feldhasen (Präsenz-Punkte) wurde im GIS eine zufällige Punktverteilung im beobachteten Landschaftsraum gegenüber gestellt (Pseudo-Absenz-Punkte). Für jeden dieser Punkte wurden bis zu 20 Minimaldistanzen zu verschiedenen Strukturelementen der Landschaft berechnet. In Generalisierten Linearen Modellen (GLM) wurden die univariaten und multivariaten Zusammenhänge der erklärenden Variablen mit der binomialen Zielvariablen modelliert. Zeitliche Aspekte der Habitatnutzung im Verlauf des Winterhalbjahres wurden mit einer multitemporalen Modellierung für zusammengefasste Zwei-Monats-Zeiträume untersucht. Die Modellselektion geschah mit Hilfe des Akaike Information Criterion (AIC). Insgesamt wurden 4.494 Standorte von Feldhasen in Opferbaum, 2.418 in Güntersleben und 1.391 in Fritzlar kartiert. Die univariate Analyse zeigt eine Meidung von Verkehrs- und Siedlungsstrukturen. Waldränder, Gehölze, Buntbrachen und Grünland werden in den Untersuchungsgebieten Fritzlar und Opferbaum bevorzugt, in Güntersleben werden die zwei letzteren gemieden. Die multivariaten Modelle zeigen eine Präferenz der Nahrungshabitate Wintergetreide und Raps, in Fritzlar und Opferbaum wird auch Grünland bevorzugt. Nach dem Nahrungshabitat wird von Feldhasen die Nähe zu potentiellen Deckungshabitaten präferiert, dabei werden nur Buntbrachen in allen Untersuchungsgebieten bevorzugt. Besonders Verkehrswege und Siedlungen werden gemiedenen, Ausnahme ist die Bevorzugung von Siedlungsbereichen in Güntersleben. Teilweise gegensätzliche Ergebnisse zeigt die Modellierung der Zwei-Monats-Zeiträume zwischen den Untersuchungsgebieten. Sie zeigen aber nur geringe Veränderungen der Habitatnutzung von Feldhasen im Verlauf des Winterhalbjahres. Allen selektierten Modellen gemein ist die geringe Erklärungsgüte von weniger als 5 % der Datenvarianz. Die Eignung der entwickelten Aufnahmemethodik und die Ergebnisse werden anhand der umfangreichen Literatur diskutiert. Die Art des Habitats ist von großer Bedeutung für die Habitatnutzung der Feldhasen. Durch die landwirtschaftliche Fruchtfolge bedingte strukturelle Veränderungen verändern ebenso die kleinräumige Habitatnutzung wie die Veränderungen der landwirtschaftlichen Schläge im Verlauf des Herbstes und Winters. Das opportunistische Habitatverhalten von Feldhasen erschwert dabei die Beobachtung von speziellem Habitatverhalten. Die zum Teil gegensätzlichen Ergebnisse werden auch vor dem Hintergrund potentieller Fehlerquellen der Methodik und einem möglichen Einfluss vernachlässigter Variablen diskutiert. Dabei stellt sich die Frage nach grundsätzlichen Konsequenzen für zukünftige Untersuchungen. Die unterschiedliche Habitatnutzung des Feldhasen in unterschiedlichen Habitaten muss sowohl bei der Wahl der Methodik als auch bei der Wahl der Gebietskulisse berücksichtigt werden. / The study presented in this thesis examined the nocturnal habitat use by hares in three different habitats. The study area Opferbaum is strongly influenced by agriculture whereas the landscape of the study area Güntersleben has very diverse structures such as groves and forest edges. The study area Fritzlar has a forest dominated landscape on the one hand and a landscape of intensive agricultural activities on the other hand. Hare locations were mapped using thermography between September 2004 to April 2005 and September 2005 to April 2006. In each of the study sites the surrounding landscape of selected viewpoints was observed once a month and hare distribution was plotted in topographical maps. Mapping of the visible landscape of the viewpoints took place during daytime. Up to 20 minimum distances to different structural elements of the landscape were calculated for each hare location (presence-points) and randomly distributed points (pseudo-absence points) in the observed landscape. Generalized linear models (GLM) were applied to model the univariate and multivariate relationships of explanatory variables with the binomial response variables (hare 1; pseudo-absence 0). Temporal aspects of habitat use during the winter were analyzed by multi-temporal modeling for combined two-month periods. The model selection was done using the Akaike Information Criterion (AIC). A total of 4,494 locations by hares were mapped in Opferbaum, 2,418 in Güntersleben and 1,391 in Fritzlar. The univariate analysis shows an avoidance of traffic and urban areas. Forest edges and groves are preferred in all study areas. Pasture and wildlife-friendly set-asides are preferred in Fritzlar and Opferbaum, but avoided in Güntersleben. The multivariate models show a preference of feeding habitats such as winter cereals and oilseed rape, hares also prefer pasture in Fritzlar and Opferbaum. After the feeding habitat, hares show a preference to be in proximity to shelter providing habitats. Wildlife-friendly set-asides were preferred in all study sites. Traffic and urban areas are avoided in Opferbaum and Fritzlar but urban areas preferred in Güntersleben. Modeling the two-month periods shows different results between the study areas but only small changes in habitat use by brown hares during the winter months. All selected models explain less than 5 % of the variance of data. The consideration of comparable studies shows that besides methodology and surveying time, the results of habitat use of brown hares are primarily influenced by the kind of the examined landscapes. The small-scale habitat use of brown hare is also influenced by structural changes in the agricultural crop rotation as well as a changing vegetation in autumn and winter. The opportunistic behaviour of brown hares make the observation of special habitat use difficult. The results are discussed in connection with error in methodology and unconsidered variables but also to fundamental consequences for future investigations. The differences in habitat use of brown hares in different habitats have to be considered in both, the choice of methodology and when choosing the study sites.

Este trabalho propõe um modelo dinâmico para análise de populações de plantas daninhas resistentes a herbicidas. O modelo representa a dinâmica populacional causada por um aumento na proporção de plantas resistentes a herbicidas, resultante da recombinação genética modificada pela pressão seletiva causada pelo herbicida. O aumento da resistência causa uma diminuição na eficácia da dose aplicada do herbicida sobre toda população e, eventualmente, compromete o controle desta população. São apresentados resultados de simulação da planta daninha Bidens subalternans, resistente ao herbicida nicosulfuron e tolerante ao herbicida atrazine, e da planta daninha Bidens pilosa, resistente ao herbicida chlorimuron-ethyl e tolerante ao herbicida imazetaphyr para diferentes doses de herbicidas. / This paper proposes a dynamic model for analysis of herbicide resistance in weed populations. The model represents population dynamic caused by an increase in the proportion of plants resistant to herbicides, resulting from genetic recombination modified by selective pressure caused by herbicide. The increase of resistance decreases the efficacy of the applied dose of herbicide over the entire population and, eventually compromises the population control. Results of simulation for different doses are presented for the weed Bidens subalternans, resistant to nicosulfuron and tolerant to atrazine, and for the weed Bidens pilosa, resistant to herbicide chlorimuron-ethyl and tolerant to imazetaphyr.

Outcomes Based Education (OBE), has been, since its inception, fraught with problems. OBE in its very nature is complex. To fully embrace this method and ensure its success, schools must be able to make the necessary paradigm shift. This can only be achieved when schools receive relevant and empowering training, support and development. In other words, organisational development must be the key words. The aim of this study is to explore the impact of organisational development interventions on the implementation of OBE. The case study method was employed where it was realised that schools that received organisational development interventions, together with Outcomes Based Education, were able to implement this method with greater understanding, skill, and confidence. The investigation recommends an organisational development design that could be used instead of the cascade model, and provides suggestions on what can be done to ensure a more successful implementation process. / Educational Studies / M. Ed (Education Management)

Cette thèse s'inscrit dans le cadre de l'analyse statistique de données en grande dimension. Nous avons en effet aujourd'hui accès à un nombre toujours plus important d'information. L'enjeu majeur repose alors sur notre capacité à explorer de vastes quantités de données et à en inférer notamment les structures de dépendance. L'objet de cette thèse est d'étudier et d'apporter des garanties théoriques à certaines méthodes d'estimation de structures de dépendance de données en grande dimension.La première partie de la thèse est consacrée à l'étude de modèles parcimonieux et aux méthodes de type Lasso. Après avoir présenté les résultats importants sur ce sujet dans le chapitre 1, nous généralisons le cas gaussien à des modèles exponentiels généraux. La contribution majeure à cette partie est présentée dans le chapitre 2 et consiste en l'établissement d'inégalités oracles pour une procédure Group Lasso appliquée aux modèles linéaires généralisés. Ces résultats montrent les bonnes performances de cet estimateur sous certaines conditions sur le modèle et sont illustrés dans le cas du modèle Poissonien. Dans la deuxième partie de la thèse, nous revenons au modèle de régression linéaire, toujours en grande dimension mais l'hypothèse de parcimonie est cette fois remplacée par l'existence d'une structure de faible dimension sous-jacente aux données. Nous nous penchons dans cette partie plus particulièrement sur la méthode PLS qui cherche à trouver une décomposition optimale des prédicteurs étant donné un vecteur réponse. Nous rappelons les fondements de la méthode dans le chapitre 3. La contribution majeure à cette partie consiste en l'établissement pour la PLS d'une expression analytique explicite de la structure de dépendance liant les prédicteurs à la réponse. Les deux chapitres suivants illustrent la puissance de cette formule aux travers de nouveaux résultats théoriques sur la PLS . Dans une troisième et dernière partie, nous nous intéressons à la modélisation de structures au travers de graphes et plus particulièrement à la détection de communautés. Après avoir dressé un état de l'art du sujet, nous portons notre attention sur une méthode en particulier connue sous le nom de spectral clustering et qui permet de partitionner les noeuds d'un graphe en se basant sur une matrice de similarité. Nous proposons dans cette thèse une adaptation de cette méthode basée sur l'utilisation d'une pénalité de type l1. Nous illustrons notre méthode sur des simulations. / This thesis falls within the context of high-dimensional data analysis. Nowadays we have access to an increasing amount of information. The major challenge relies on our ability to explore a huge amount of data and to infer their dependency structures.The purpose of this thesis is to study and provide theoretical guarantees to some specific methods that aim at estimating dependency structures for high-dimensional data. The first part of the thesis is devoted to the study of sparse models through Lasso-type methods. In Chapter 1, we present the main results on this topic and then we generalize the Gaussian case to any distribution from the exponential family. The major contribution to this field is presented in Chapter 2 and consists in oracle inequalities for a Group Lasso procedure applied to generalized linear models. These results show that this estimator achieves good performances under some specific conditions on the model. We illustrate this part by considering the case of the Poisson model. The second part concerns linear regression in high dimension but the sparsity assumptions is replaced by a low dimensional structure underlying the data. We focus in particular on the PLS method that attempts to find an optimal decomposition of the predictors given a response. We recall the main idea in Chapter 3. The major contribution to this part consists in a new explicit analytical expression of the dependency structure that links the predictors to the response. The next two chapters illustrate the power of this formula by emphasising new theoretical results for PLS. The third and last part is dedicated to graphs modelling and especially to community detection. After presenting the main trends on this topic, we draw our attention to Spectral Clustering that allows to cluster nodes of a graph with respect to a similarity matrix. In this thesis, we suggest an alternative to this method by considering a $l_1$ penalty. We illustrate this method through simulations.

Made available in DSpace on 2016-06-02T20:06:09Z (GMT). No. of bitstreams: 1 5914.pdf: 1420398 bytes, checksum: e5c2a5d7845b5b17b94959ce849fd613 (MD5) Previous issue date: 2014-04-01 / Financiadora de Estudos e Projetos / The real estate market has a key role in the country and counties economy attracting several studies and researches that explains and interpret the numerous transactions performed, and especially to find appropriate ways to define the monetary value. Usually the real estate data modeling is performed through regression models, especially the linear and also the generalized linear models ( Nelder andWedderburn, 1972). Because these data has different characteristics such as heteroscedasticity, non-normality and heterogeneity, the use of these models can suffer limitations, so it is appropriate to use more and more complex models, such as generalized additive models for location, scale and shape GAMLSS (proposed by Rigby & Stasinopoulos (2005), that allows all parameters of the response variable are modeled parametric or non parametric form. In this context and based on a dataset of urban land of São Carlos city in 2005 was estimated the empirical function the value of the land addressing the class of linear models, generalized linear models and the GAMLSS. Alternatively, considering the existence of two types of real estate prices: already sold (observed) and announced (censored), was proposed to the data, using the survival analysis considering censored left and the GAMLSS in the parameter estimation process. A simulation study and a study of local influence was also performed. / O mercado imobiliário possui um papel fundamental na economia do país e municípios atraindo diversos estudos e pesquisas que buscam explicar e interpretar as inúmeras transações realizadas, e principalmente, encontrar maneiras adequadas de determinar seu valor monetário. Geralmente a modelagem de dados imobiliários e feita por meio de modelos de regressão, especialmente os lineares e também, os modelos lineares generalizados (Nelder e Wedder-burn,1972). Por se tratarem de dados com diferentes características, como heterocedasticidade, não normalidade e heterogeneidade, o uso desses modelos podem sofrer limitações, por isso torna-se adequada a utilização de modelos cada vez mais complexos, como por exemplo, os modelos aditivos generalizados para posição, escala e forma (GAMLSS) propostos por Rigby & Stasinopoulos (2005), que permitem que todas as estimativas dos parâmetros envolvidos no modelo sejam obtidas de forma paramétrica ou não-paramétrica. Neste contexto e com base em um conjunto de dados de lotes urbanos da cidade de Sao Carlos do ano de 2005 foi estimado a função empírica do valor de lotes abordando a classe de modelos lineares, modelos lineares generalizados e o GAMLSS. Alternativamente, considerando a existência de dois tipos de preços de imóveis: ja vendidos (observados) e anunciados (censurados), foi proposto aos dados, a utilização da analise de sobrevivência considerando censura a esquerda e o GAMLSS no processo de estimação dos parâmetros. Foi realizado também um estudo de simulação e um estudo de influência local.

abstract: Through a two study simulation design with different design conditions (sample size at level 1 (L1) was set to 3, level 2 (L2) sample size ranged from 10 to 75, level 3 (L3) sample size ranged from 30 to 150, intraclass correlation (ICC) ranging from 0.10 to 0.50, model complexity ranging from one predictor to three predictors), this study intends to provide general guidelines about adequate sample sizes at three levels under varying ICC conditions for a viable three level HLM analysis (e.g., reasonably unbiased and accurate parameter estimates). In this study, the data generating parameters for the were obtained using a large-scale longitudinal data set from North Carolina, provided by the National Center on Assessment and Accountability for Special Education (NCAASE). I discuss ranges of sample sizes that are inadequate or adequate for convergence, absolute bias, relative bias, root mean squared error (RMSE), and coverage of individual parameter estimates. The current study, with the help of a detailed two-part simulation design for various sample sizes, model complexity and ICCs, provides various options of adequate sample sizes under different conditions. This study emphasizes that adequate sample sizes at either L1, L2, and L3 can be adjusted according to different interests in parameter estimates, different ranges of acceptable absolute bias, relative bias, root mean squared error, and coverage. Under different model complexity and varying ICC conditions, this study aims to help researchers identify L1, L2, and L3 sample size or both as the source of variation in absolute bias, relative bias, RMSE, or coverage proportions for a certain parameter estimate. This assists researchers in making better decisions for selecting adequate sample sizes in a three-level HLM analysis. A limitation of the study was the use of only a single distribution for the dependent and explanatory variables, different types of distributions and their effects might result in different sample size recommendations. / Dissertation/Thesis / Doctoral Dissertation Educational Psychology 2016

Made available in DSpace on 2016-12-08T16:44:34Z (GMT). No. of bitstreams: 1 PGPV11MA035.pdf: 382044 bytes, checksum: 888904b44ab1d4e73c30ea19d52dffc8 (MD5) Previous issue date: 2011-02-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Bean production is affected by a range of abiotic stresses such as drought, low soil fertility, soil acidity and unfavorable temperatures. However, a deep root system and well distributed allows better adaptation, especially regarding the conditions of drought and low nutrient availability. Despite the advancement of research, little has been done in this line of study. Thus, this study aimed to: i) present an alternative to the measurement and statistical analysis to the root distribution, ii) measuring the character of root distribution in bean genotypes Active Germplasm Bank of UDESC (Universidade do Estado de Santa Catarina) and its relationship to other important agronomic characteristics, and iii) to evaluate the root distribution along the profile between mutant populations and select bean genotypes with higher metric values for the character. The experiments were performed in the experimental area of IMEGEM, arranged in a randomized design. The evaluation of root distribution was held in hybrids, cultivars, accessions and mutant populations of beans. For that were open profiles perpendicular to the plant rows of beans, where a rectangle with dimensions of 0.5 m wide by 0.3 m, 0.05 m grid was set aside and a photo was taken. The determination of root distribution in the binary system (name of presence (1) and absence (0) of roots in each box) was performed by analysis of the photo. It was observed that the measurement of the characteristic root distribution through the determination of simple events is a valuable tool for researchers because it allows the quantitative analysis of root distribution by means of Generalized Linear Models the GENMOD procedure of SAS. Considering the small number of genotypes, can be stated that the Active Germplasm Bank Bean has promising genotypes for the character root distribution, where BAF09 (black) and BAF35 (carioca) present the best root deep distribution (20 to 30 cm). It might still be verified the presence of significant positive correlation between root distribution and other traits of agronomic importance. The mutant populations present different performance against the mutagenic for the root distribution. Since the most promising segregating populations are derived from cultivars IPR Uirapuru and IPR Chopim, as they present a significant increase in the number of roots with increasing doses of mutagen / A produção de feijão é afetada por uma gama de estresses abióticos, como seca, baixa fertilidade do solo, acidez do solo e temperaturas desfavoráveis. No entanto, um sistema radicular profundo e bem distribuído permite melhor adaptação da cultura, principalmente no que tange as condições de deficiência hídrica e baixa disponibilidade de nutrientes. Apesar do avanço da pesquisa, pouco se tem trabalhado nessa linha de estudo. Sendo assim, este trabalho teve como objetivos: i) apresentar uma alternativa para a mensuração e a análise estatística para o caráter distribuição radicular; ii) mensurar o caráter distribuição radicular em genótipos de feijão do Banco Ativo de Germoplasma da UDESC (Universidade do Estado de Santa Catarina) e verificar a correlação com outros caracteres de importância agronômica; e iii) avaliar a distribuição radicular ao longo do perfil entre populações mutantes e selecionar genótipos de feijão com valores métricos superiores para o caráter. Os experimentos foram realizados na área experimental do IMEGEM, arranjados em delineamento inteiramente casualizado. A avaliação da distribuição radicular foi realizada em híbridos, cultivares, acessos e populações mutantes de feijão. Para isso foram abertos perfis perpendiculares alinha de semeadura, onde um retângulo com dimensões de 0,5 m de largura por 0,3 m de altura, quadriculado com 0,05 m de lado foi disposto e uma foto foi capturada. A determinação da distribuição radicular no sistema binário (denominação de presença (1) e ausência (0) das raízes em cada quadrícula) foi realizada por meio da análise da foto. A mensuração da característica distribuição radicular a partir da determinação de eventos simples (presença=1 e ausência=0) é uma valiosa ferramenta para o pesquisador, já que possibilita a análise quantitativa da distribuição radicular, por meio dos Modelos Lineares Generalizados do procedimento GENMOD do SAS. Ainda que de forma incipiente, devido ao pequeno número de genótipos avaliados pode ser afirmado que o Banco Ativo de Germoplasma de Feijão possui genótipos promissores para o caráter distribuição radicular. Sendo que BAF09 (preto) e BAF35 (carioca), por apresentarem distribuição radicular profunda e significativa (20 a 30 cm), merecem destaque. Pôde ser verificada ainda a presença de correlação positiva e significativa entre a distribuição radicular e outros caracteres de importância agronômica. As populações mutantes apresentaram desempenho diferenciado frente ao mutagênico para a distribuição radicular. As populações segregantes mais promissoras foram oriundas das cultivares IPR Uirapuru e IPR Chopim, pois apresentaram um aumento significativo na distribuição radicular o aumento das doses do agente mutagênico