Este trabalho busca compreender e mensurar as características dos produtores rurais que ampliam a probabilidade para que o mesmo tenha acesso ao crédito rural. Utilizando os dados do Levantamento das Unidades Produtivas Agropecuárias (LUPA) do Estado de São Paulo (2006/2007), que abrange todas as UPAs pertencentes aos 645 municípios do estado, foi possível detalhar as características observáveis dos produtores e das propriedades que acessaram o crédito rural em 2007. Para tanto, foi utilizado o método de Árvores de Classificação e Regressão. As estimações realizadas para todas as UPAs de estado de São Paulo mostraram que a diferença de tamanho das unidades produtivas é o principal determinante para o acesso ao crédito. Quando se analisa o acesso ao crédito para unidades produtivas de pequena, média e grande extensão, algumas variáveis apresentam maior impacto no acesso ao crédito. Para as unidades de pequena extensão (até dez hectares), a diversificação de cultura, entre cultura temporária e perene, aumenta a probabilidade dos produtores acessarem o crédito. Para propriedades de média extensão (até quinhentos hectares), a presença de vínculos institucionais, seja cooperativa, sindicato ou associação, e melhorias em gestão (uso de computador, acesso á assistência técnica oficial), além da diversificação de cultura, elevam as probabilidades de acesso ao crédito. A análise mostra também que para unidades produtivas de grande extensão as variáveis que impactam a probabilidade de acesso ao crédito rural estão relacionadas a participação em instituições (cooperado e associado), além de variáveis relacionadas à melhoria de gestão, independentemente do tipo de cultura cultivada pela UPA. / This paper aims to understand and measure the characteristics of farmers which enhance their likelihood of having access to rural credit. Using data from the Survey of Agricultural Production Units (LUPA, in portuguese) of São Paulo (2006/2007), which covers all 645 Agricultural Production Units belonging to municipalities in the state was possible to detail the observable characteristics and properties of the producers who have accessed rural credit in 2007. For this, we used the Classification and Regression Trees method. The estimates performed for all UPAs (in Portuguese) in the state of São Paulo showed that the difference in size of production units is the main determinant to access credit. When analyzing the access to credit for production units of small, medium and large extent, some variables have greater impact on access to credit. For units of small extent (up to ten hectares) the culture diversification between temporary and perennial crop, increases the likelihood of farmers to access credit. For production units of medium length (up to five hundred acres), the presence of institutional links, such as cooperative, union or association, and improvements in management (computer use, technical support officer access), and crop diversification, increase the likelihood of access to credit. The analysis also shows that for production units with large extent the variables that have more impact in the probability of access to rural credit are related to participation in institutions (cooperative and associate), and variables related to improvement management, regardless of the type of crop cultivated by UPA.

The forest products industry is a significant contributor to the U.S. economy contributing six percent of the total U.S. manufacturing gross domestic product (GDP), placing it on par with the U.S. automotive and plastics industries. Sustaining business competitiveness by reducing costs and maintaining product quality will be essential in the long term for this industry. Improved production efficiency and business competitiveness is the primary rationale for this work. A challenge facing this industry is to develop better knowledge of the complex nature of process variables and their relationship with final product quality attributes. Quantifying better the relationships between process variables (e.g., press temperature) and final product quality attributes plus predicting the strength properties of final products are the goals of this study. Destructive lab tests are taken at one to two hour intervals to estimate internal bond (IB) tensile strength and modulus of rupture (MOR) strength properties. Significant amounts of production occur between destructive test samples. In the absence of a real-time model that predicts strength properties, operators may run higher than necessary feedstock input targets (e.g., weight, resin, etc.). Improved prediction of strength properties using boosted regression tree (BRT) models may reduce the costs associated with rework (i.e., remanufactured panels due to poor strength properties), reduce feedstocks costs (e.g., resin and wood), reduce energy usage, and improve wood utilization from the valuable forest resource. Real-time, temporal process data sets were obtained from a U.S. particleboard manufacturer. In this thesis, BRT models were developed to predict the continuous response variables MOR and IB from a pool of possible continuous predictor variables. BRT model comparisons were done using the root mean squared error for prediction (RMSEP) and the RMSEP relative to the mean of the response variable as a percent (RMSEP%) for the validation data set(s). Overall, for MOR, RMSEP values ranged from 0.99 to 1.443 MPa, and RMSEP% values ranged from 7.9% to 11.6%. Overall, for IB, RMSEP values ranged from 0.074 to 0.108 MPa, and RMSEP% values ranged from 12.7% to 18.6%.

Although forest properties are known to influence snowpack accumulation and spring runoff, the processes underlying the impacts of forest canopy cover on the input of snowmelt to the catchment remain poorly characterized. In this study I show that throughfall and canopy shading can combine to result in maximal snowpacks in forests of moderate canopy density. Snow depth and density data taken shortly before spring melt in the Jemez Mountains of New Mexico show strong correlation between forest canopy density and snow water equivalent, with maximal snow accumulation in forests with density between 25 and 45%. Forest edges are also shown to be highly influential on local snow depth variability, with shaded open areas holding significantly deeper snow than either unshaded open or deep forest areas. These results are broadly applicable in improving estimates of water resource availability, predicting the ecohydrological implications of vegetation change, and informing integrated water resources management.

No description available.

Little is known about the distribution of black corals in the northwestern Gulf of Mexico. Of thirty-nine species of black coral documented in the Western Atlantic, thirty have been previously documented by various studies in the Gulf of Mexico. This study proposes potential range extensions for four black coral species, including Stichopathes gracilis, Stichopathes semiglabra, Tanacetipathes paula, and Tanacetipathes spinescens, to include the Gulf of Mexico. The validation of in situ identifications of black coral species is evaluated, and recommendations for species identifications and species groupings are made. Black coral associated fauna are documented, supporting known associations and documenting potentially new associations and species. Habitat suitability models for the distribution of black coral species at selected banks in the northwestern Gulf of Mexico were generated. Presence-only models made using the MaxEnt modeling program were compared to presence-absence models made using Boosted Regression Tree modeling techniques. Presence-absence models were documented to have greater predictive accuracy than the presence-only models, which showed evidence of model overfitting. The model was projected to five similar salt-dome features in the region, highlighting extensive habitat for multiple black coral species in these unexplored habitats. This study presents habitat suitability maps as a testable hypothesis for black coral distribution in the mesophotic zone of this region.

In this thesis I examined the impacts of variable retention harvesting on residual tree mortality and natural regeneration of white spruce [Picea glauca (Moench (Voss)] in northern Alberta. The VR was done in four overstory canopy compositions (ranging from deciduous dominated to conifer dominated) and at six rates of canopy retention (2%, 10%, 20%, 50%, 75% and 100%). After 10 years there was 32.9 % mortality of aspen (Populus tremuloides Michx.) and 16.9 % mortality of spruce in the VR cuts. Mortality of individual trees was greater with low density of trees, in the conifer stands and for trees with short live crowns, which are large and trees near machine corridors. Natural regeneration of spruce was greatest with higher availability of seed trees (>30 ha-1) and on machine corridors, where stocking reached 74%. By contrast, stocking was 14% on retention strips, when seed tree density was 11 seed trees ha-1. / Forest Biology and Management

Made available in DSpace on 2015-03-26T13:32:19Z (GMT). No. of bitstreams: 1 texto completo.pdf: 677917 bytes, checksum: fe42f6008c42e03314bd889ebb94ea29 (MD5) Previous issue date: 2013-03-15 / The process of selection of clones to be used as new commercial varieties is lacking in statistical methods and phytotechnical that increase the genetic gains in crop improvement programs of cane sugar. Out of five stages of the selection program to improve the RIDESA, the first one, T1, is endowed with great importance. First because it develops the remaining phases of the crop breeding program. And second because the volume of information to be analyzed is huge. Thus, if the data is not properly analyzed, good clones can be ruled out in the early stages, reducing the excellence of the program. The common strategies of selection, BLUP and BLUPIS, have the disadvantage of the necessity of weighing the whole plot. One way around this is to categorize the components of production; stalk height, stalk diameter and number of stalks, by decision trees. Through these trees, you can generate the combinations of these yield components and their production values. Using data from commercial varieties to generate the trees, it would not be necessary to weigh the entire plot, saving time and money. The aim of this study was to evaluate the categorization of yield components as selection strategy between and within families by comparing their performance with the usual methods, BLUP and BLUPIS. The algorithm used was the CART. Nonparametric by nature, it`s capable of producing binary divisions combining the explanatory variables and associating them with different response values. Data were collected from 5 experiments, installed in May 2007 in a randomized block design, with each experiment consisting of 5 blocks, 22 families and two commercial varieties. CART algorithm was effective in defining classes of yield components followed by selection of the best families with mean accuracy of 73% when compared with BLUPIS and BLUP. / O processo de seleção de clones de cana-de-açúcar é carente de métodos fitotécnicos e estatísticos que elevem os ganhos genéticos nos programas de melhoramento da cultura da cana-de-açúcar. De cinco fases de seleção do programa de melhoramento da RIDESA, a primeira é dotada de grande importância porque dela se desenvolvem as demais fases do programa e porque o volume de informações a ser analisados é enorme. Assim, caso os dados não forem corretamente analisados, pode-se descartar bons materiais logo nas primeiras fases, diminuindo a excelência do programa. As estratégias usuais de seleção, BLUP e BLUPIS, têm a desvantagem de exigir a de pesagem de toda a parcela. Uma maneira de se contornar isso é categorizar os componentes de produção; altura de colmos, diâmetro de colmos e número de colmos, via árvores de decisão. Através dessas árvores, é possível gerar as combinações desses componentes de produção e os respectivos valores de produção. Utilizando dados de testemunhas para gerar as árvores, não seria necessária a pesagem de toda a parcela, economizando tempo e recursos financeiros. O objetivo desse trabalho foi avaliar a categorização dos componentes de produção como estratégia de seleção entre e dentro de famílias através da comparação de seu desempenho com os métodos usuais, BLUP e BLUPIS. O algoritmo de árvore utilizado foi o CART. De natureza não paramétrica, esse é capaz de produzir divisões binárias combinando as variáveis explicativas e associando-as com distintos valores de resposta. Os dados foram coletados de 5 experimentos, instalados em maio de 2007, no delineamento em blocos casualizados, sendo cada experimento constituído de 5 blocos, 22 famílias e 2 testemunhas. O algoritmo CART foi eficiente em definir as classes dos componentes de produção seguido da seleção das melhores famílias no campo com acurácia média próxima de 73% quando comparado com o BLUPIS e BLUP.

Este trabalho busca compreender e mensurar as características dos produtores rurais que ampliam a probabilidade para que o mesmo tenha acesso ao crédito rural. Utilizando os dados do Levantamento das Unidades Produtivas Agropecuárias (LUPA) do Estado de São Paulo (2006/2007), que abrange todas as UPAs pertencentes aos 645 municípios do estado, foi possível detalhar as características observáveis dos produtores e das propriedades que acessaram o crédito rural em 2007. Para tanto, foi utilizado o método de Árvores de Classificação e Regressão. As estimações realizadas para todas as UPAs de estado de São Paulo mostraram que a diferença de tamanho das unidades produtivas é o principal determinante para o acesso ao crédito. Quando se analisa o acesso ao crédito para unidades produtivas de pequena, média e grande extensão, algumas variáveis apresentam maior impacto no acesso ao crédito. Para as unidades de pequena extensão (até dez hectares), a diversificação de cultura, entre cultura temporária e perene, aumenta a probabilidade dos produtores acessarem o crédito. Para propriedades de média extensão (até quinhentos hectares), a presença de vínculos institucionais, seja cooperativa, sindicato ou associação, e melhorias em gestão (uso de computador, acesso á assistência técnica oficial), além da diversificação de cultura, elevam as probabilidades de acesso ao crédito. A análise mostra também que para unidades produtivas de grande extensão as variáveis que impactam a probabilidade de acesso ao crédito rural estão relacionadas a participação em instituições (cooperado e associado), além de variáveis relacionadas à melhoria de gestão, independentemente do tipo de cultura cultivada pela UPA. / This paper aims to understand and measure the characteristics of farmers which enhance their likelihood of having access to rural credit. Using data from the Survey of Agricultural Production Units (LUPA, in portuguese) of São Paulo (2006/2007), which covers all 645 Agricultural Production Units belonging to municipalities in the state was possible to detail the observable characteristics and properties of the producers who have accessed rural credit in 2007. For this, we used the Classification and Regression Trees method. The estimates performed for all UPAs (in Portuguese) in the state of São Paulo showed that the difference in size of production units is the main determinant to access credit. When analyzing the access to credit for production units of small, medium and large extent, some variables have greater impact on access to credit. For units of small extent (up to ten hectares) the culture diversification between temporary and perennial crop, increases the likelihood of farmers to access credit. For production units of medium length (up to five hundred acres), the presence of institutional links, such as cooperative, union or association, and improvements in management (computer use, technical support officer access), and crop diversification, increase the likelihood of access to credit. The analysis also shows that for production units with large extent the variables that have more impact in the probability of access to rural credit are related to participation in institutions (cooperative and associate), and variables related to improvement management, regardless of the type of crop cultivated by UPA.

Tree-based methods are a nice add-on to traditional statistical methods when solving classification and regression problems. The aim of this master thesis is not to judge which approach is better but rather bring the overview of these methods and apply them on the real data using R. Focus is made especially on the basic methodology of tree-based models and the application in specific software in order to provide wide range of tool for reader to be able to use these methods. One part of the thesis touches the advanced tree-based methods to provide full picture of possibilities.

No description available.