Potato is cultivated in subtropical, during spring and autumn, and temperate conditions, during summer, of the Southern area of Brazil. These different growing conditions affect tuber yield and processing quality. The objectives of this work were to select potato clones with high tuber yield and processing quality and adapted to subtropical and temperate growing conditions; and to develop a breeding strategy to early identify superior clones. We made crosses to develop and early select clones adapted to two growing seasons. Advanced clones were selected for adaptation to subtropical and temperate conditions of the Southern region of Brazil. Yield per hill, tuber appearance, chip color and dry matter and reduced sugar content were evaluated in all experiments. The experiments were a factorial (clones x seasons) in a complete random design, with two or three replications. The highest yield per hill was gotten during summer season of template conditions. Tuber appearance was not affected by season and site growing conditions. The unfavorable conditions of autumn resulted in dark chip color and high content of reduced sugars. Dry matter content varies among environments, but the lowest content was during autumn season. High genetic variability was gotten for all evaluated traits, which enable to select clones better than the check varieties. The first clonal selection should be done during summer season to identify potential clones adapted to subtropical and temperate conditions. Autumn growing conditions are not favorable to high yield and quality and increase dormancy period of the tubers, but this kind of environment is necessary to select clones adapted to two seasons. The clones SJSM01274-4, SJSM01212-2, SJSM00211-3, SJSM99159-8 and SJSM03478-37 have high yield and processing quality in both subtropical and temperate conditions of the Southern area of Brazil. / Na região Sul do Brasil, a batata é cultivada em condições subtropical, durante a primavera e outono, e temperada, durante o verão, que afetam a produtividade e a qualidade para processamento industrial. Os objetivos deste trabalho foram selecionar clones de batata com alta produtividade e qualidade para processamento e adaptados às condições subtropical e temperada de cultivo e desenvolver uma estratégia de melhoramento para a identificação precoce de clones superiores. Foram realizados cruzamentos e efetuada a seleção precoce de clones adaptados para dois cultivos anuais e clones avançados foram selecionados para ampla adaptação à condições subtropical e temperada de cultivo da região Sul do Brasil. A produtividade por cova, a aparência dos tubérculos, a cor de chips e os teores de massa seca e açúcares redutores foram avaliados nos diferentes experimentos. Os experimentos foram conduzidos em um fatorial (clones x épocas de cultivo) em blocos ao acaso, com duas ou três repetições. A produtividade por cova foi mais elevada no cultivo de verão, em condição temperada de cultivo. A aparência dos tubérculos foi similar entre as épocas e locais de cultivo. A cor de chips e o teor de açúcares redutores foram inferiores no cultivo de outono, pela condição menos favorável. O teor de massa seca foi variável entre os ambientes, com menores teores no cultivo de outono. Para todos os caracteres avaliados houve alta variabilidade genética, que possibilitou a seleção de clones superiores à melhor testemunha. A primeira geração de seleção realizada no cultivo de verão possibilita a identificação precoce de clones, com potencial de adaptação para as condições subtropical e temperada. As condições de cultivo de outono são menos favoráveis para a produtividade e qualidade e prolongam o período de dormência dos tubérculos, porém esse ambiente é necessário para a seleção de clones para dois cultivos anuais. Os clones SJSM01274-4, SJSM01212-2, SJSM00211-3, SJSM99159-8 e SJSM03478-37 reúnem, de forma equilibrada, os caracteres de produtividade e qualidade de processamento industrial para as condições subtropicais e temperadas de cultivo do Sul do Brasil.

Die hier vorliegende Forschungsarbeit hat zum Ziel, Erkenntnisse über den Einfluss der Vorverarbeitung auf die Ergebnisse der Wissensgenerierung zu gewinnen und konkrete Handlungsempfehlungen für die geeignete Vorverarbeitung von Textkorpora in Text Data Mining (TDM) Vorhaben zu geben. Der Fokus liegt dabei auf der Extraktion und der Verfolgung von Konzepten innerhalb bestimmter Wissensdomänen mit Hilfe eines methodischen Ansatzes, der auf der waagerechten und senkrechten Segmentierung von Korpora basiert. Ergebnis sind zeitlich segmentierte Teilkorpora, welche die Persistenzeigenschaft der enthaltenen Terme widerspiegeln. Innerhalb jedes zeitlich segmentierten Teilkorpus können jeweils Cluster von Termen gebildet werden, wobei eines diejenigen Terme enthält, die bezogen auf das Gesamtkorpus nicht persistent sind und das andere Cluster diejenigen, die in allen zeitlichen Segmenten vorkommen. Auf Grundlage einfacher Häufigkeitsmaße kann gezeigt werden, dass allein die statistische Qualität eines einzelnen Korpus es erlaubt, die Vorverarbeitungsqualität zu messen. Vergleichskorpora sind nicht notwendig. Die Zeitreihen der Häufigkeitsmaße zeigen signifikante negative Korrelationen zwischen dem Cluster von Termen, die permanent auftreten, und demjenigen das die Terme enthält, die nicht persistent in allen zeitlichen Segmenten des Korpus vorkommen. Dies trifft ausschließlich auf das optimal vorverarbeitete Korpus zu und findet sich nicht in den anderen Test Sets, deren Vorverarbeitungsqualität gering war. Werden die häufigsten Terme unter Verwendung domänenspezifischer Taxonomien zu Konzepten gruppiert, zeigt sich eine signifikante negative Korrelation zwischen der Anzahl unterschiedlicher Terme pro Zeitsegment und den einer Taxonomie zugeordneten Termen. Dies trifft wiederum nur für das Korpus mit hoher Vorverarbeitungsqualität zu. Eine semantische Analyse auf einem mit Hilfe einer Schwellenwert basierenden TDM Methode aufbereiteten Datenbestand ergab signifikant unterschiedliche Resultate an generiertem Wissen, abhängig von der Qualität der Datenvorverarbeitung. Mit den in dieser Forschungsarbeit vorgestellten Methoden und Maßzahlen ist sowohl die Qualität der verwendeten Quellkorpora, als auch die Qualität der angewandten Taxonomien messbar. Basierend auf diesen Erkenntnissen werden Indikatoren für die Messung und Bewertung von Korpora und Taxonomien entwickelt sowie Empfehlungen für eine dem Ziel des nachfolgenden Analyseprozesses adäquate Vorverarbeitung gegeben. / The research work available here has the goal of analysing the influence of pre-processing on the results of the generation of knowledge and of giving concrete recommendations for action for suitable pre-processing of text corpora in TDM. The research introduced here focuses on the extraction and tracking of concepts within certain knowledge domains using an approach of horizontally (timeline) and vertically (persistence of terms) segmenting of corpora. The result is a set of segmented corpora according to the timeline. Within each timeline segment clusters of concepts can be built according to their persistence quality in relation to each single time-based corpus segment and to the whole corpus. Based on a simple frequency measure it can be shown that only the statistical quality of a single corpus allows measuring the pre-processing quality. It is not necessary to use comparison corpora. The time series of the frequency measure have significant negative correlations between the two clusters of concepts that occur permanently and others that vary within an optimal pre-processed corpus. This was found to be the opposite in every other test set that was pre-processed with lower quality. The most frequent terms were grouped into concepts by the use of domain-specific taxonomies. A significant negative correlation was found between the time series of different terms per yearly corpus segments and the terms assigned to taxonomy for corpora with high quality level of pre-processing. A semantic analysis based on a simple TDM method with significant frequency threshold measures resulted in significant different knowledge extracted from corpora with different qualities of pre-processing. With measures introduced in this research it is possible to measure the quality of applied taxonomy. Rules for the measuring of corpus as well as taxonomy quality were derived from these results and advice suggested for the appropriate level of pre-processing.