Toward a Global Organic Culture: Ethnographic Perspectives on Wwoofing Practices in Peru and New Zealand

In this thesis, I explore the meaning behind sustainable living among organic farmers and their families in two countries. It is based on original, ethnographic research that I conducted in New Zealand in fall 2012 and Peru in summer 2012 with support from the Department of Sociology and Anthropology Meerwarth Undergraduate Research Fund. In carrying out my research I relied on participant-observation, semi-structured interviews, focus groups, and writing ethnographic fieldnotes. Drawing on contemporary scholarship in the anthropology of food and the environment, my thesis contributes to cross-culturally understandings of sustainability and local and global foodways. Specifically, I will interpret the meaning and significance of my informants’ decision to live sustainably through their participation in wwoofing. The global network of wwoofing aims to connect volunteers interested in learning about organic farming techniques with farmers looking for labor assistance. Volunteers exchange work for food, accommodation, knowledge, and experience. As a method of farming and a subjective ideological orientation, this global movement allows travelers from all over the world to experience organic lifestyles worldwide. In my thesis, I connect my experiences of organic living in Peru and New Zealand. In comparing wwoofing practices in these two field sites, I argue that despite observable differences in organic practices, a global organic culture is emerging. Here I highlight some shared features of this global organic culture, such as food authenticity, sustainability of the earth, and a personal connection of individuals to the land. The global organic culture emphasizes a conscious awareness of what is going into one’s body and why. Using food as an expression of values and beliefs, organic farmers reconnect to the land and their food in attempts to construct an alternative identity. By focusing on food authenticity, my informants develop vast relationships with the land, which shapes their identity and creates new forms of self-enhancement.

Meta-Environment and Executable Meta-Language using Smalltalk: an Experience Report

Object-oriented modelling languages such as EMOF are often used to specify domain specific meta-models. However, these modelling languages lack the ability to describe behavior or operational semantics. Several approaches have used a subset of Java mixed with OCL as executable meta-languages. In this experience report we show how we use Smalltalk as an executable meta-language in the context of the Moose reengineering environment. We present how we implemented EMOF and its behavioral aspects. Over the last decade we validated this approach through incrementally building a meta-described reengineering environment. Such an approach bridges the gap between a code-oriented view and a meta-model driven one. It avoids the creation of yet another language and reuses the infrastructure and run-time of the underlying implementation language. It offers an uniform way of letting developers focus on their tasks while at the same time allowing them to meta-describe their domain model. The advantage of our approach is that developers use the same tools and environment they use for their regular tasks. Still the approach is not Smalltalk specific but can be applied to language offering an introspective API such as Ruby, Python, CLOS, Java and C#.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Homogenization of Portuguese long-term temperature data series: Lisbon, Coimbra and Porto

Three long-term temperature data series measured in Portugal were studied to detect and correct non-climatic homogeneity breaks and are now available for future studies of climate variability. Series of monthly minimum (Tmin) and maximum (Tmax) temperatures measured in the three Portuguese meteorological stations of Lisbon (from 1856 to 2008), Coimbra (from 1865 to 2005) and Porto (from 1888 to 2001) were studied to detect and correct non-climatic homogeneity breaks. These series together with monthly series of average temperature (Taver) and temperature range (DTR) derived from them were tested in order to detect homogeneity breaks, using, firstly, metadata, secondly, a visual analysis and, thirdly, four widely used homogeneity tests: von Neumann ratio test, Buishand test, standard normal homogeneity test and Pettitt test. The homogeneity tests were used in absolute (using temperature series themselves) and relative (using sea-surface temperature anomalies series obtained from HadISST2 close to the Portuguese coast or already corrected temperature series as reference series) modes. We considered the Tmin, Tmax and DTR series as most informative for the detection of homogeneity breaks due to the fact that Tmin and Tmax could respond differently to changes in position of a thermometer or other changes in the instrument's environment; Taver series have been used, mainly, as control. The homogeneity tests show strong inhomogeneity of the original data series, which could have both internal climatic and non-climatic origins. Homogeneity breaks which have been identified by the last three mentioned homogeneity tests were compared with available metadata containing data, such as instrument changes, changes in station location and environment, observing procedures, etc. Significant homogeneity breaks (significance 95% or more) that coincide with known dates of instrumental changes have been corrected using standard procedures. It was also noted that some significant homogeneity breaks, which could not be connected to the known dates of any changes in the park of instruments or stations location and environment, could be caused by large volcanic eruptions. The corrected series were again tested for homogeneity: the corrected series were considered free of non-climatic breaks when the tests of most of monthly series showed no significant (significance 95% or more) homogeneity breaks that coincide with dates of known instrument changes. Corrected series are now available in the frame of ERA-CLIM FP7 project for future studies of climate variability.

Chemical and isotopical analsyes of DSDP Hole 34-321 basalts

A blue-green smectite (iron-rich saponite) and green mica (celadonite) are the dominant sheet silicates in veins within the 10.5 m of basalt cored during DSDP Leg 34, Site 32l, in the Nazca plate. Oxygen isotopic analyses of these clays, and associated calcite, indicate a formation temperature of <25°C. Celadonite contains appreciable Fe2O3, K2O and SiO2, intermediate MgO, and very little Al2O3. Celadonite is commonly associated with goethite and hematite, which suggests that this phase formed by precipitation within a dominantly oxygenated environment of components leached from basalt and provided by seawater. A mass balance estimate indicates that celadonite formation can remove no more than 15% of the K annually transported to the oceans by rivers. In contrast, iron-rich saponite containing significant Al2O3 appears to have precipitated from a nonoxidizing, distinctly alkaline fluid containing a high Na/K ratio relative to unmodified seawater. Seawater-basalt interaction at low temperatures, resulting in the formation of celadonite and smectite may explain chemical gradients observed in interstitial waters of sediments overlying basalts.

Geochemical and Nd isotopic investigations of sediments from the South China Sea

Secular variations in geochemistry and Nd isotopic data have been documented in sediment samples at ODP Site 1148 in the South China Sea. Major and trace elements show significant changes at ca. 29.5 Ma and 26-23 Ma, whereas epsilon-Nd values show a single change at ca. 26-23 Ma. Increases in Al/Ti, Al/K, Rb/Sr, and La/Lu ratios and a decrease in the Th/La ratio of the sediments beginning at 29.5 Ma are consistent with more intense chemical weathering in the source region. The abrupt change in Nd isotopes and geochemistry at ca. 26-23 Ma coincides with a major discontinuity in the sedimentology and physical properties of the sediments, implying a drastic change in sedimentary provenance and environment at the drill site. Comparison of the Nd isotopes of sediments from major rivers flowing into the South China Sea suggests that pre-27 Ma sediments were dominantly derived from a southwestern provenance (Indochina-Sunda Shelf and possibly northwestern Borneo), whereas post-23 Ma sediments were derived from a northern provenance (South China). This change in provenance from southwest to north was largely caused by ridge jumping during seafloor spreading of the South China Sea, associated with a southwestward expansion of the ocean basin crust and a global rise in sea level. Thus, the geochemical and Nd isotopic changes in the sediments at ODP Site 1148 are interpreted as a response to a major plate reorganization in SE Asia at ca. 25 Ma.

Alkenones and TEX86 temperature proxies for core-top sediments from the southern Adriatic Sea

The Mediterranean Sea is at the transition between temperate and tropical air masses and as such of importance for studying climate change. The Gulf of Taranto and adjacent SW Adriatic Sea are at the heart of this region. Their sediments are excellently suited for generating high quality environmental records for the last millennia with a sub-decadal resolution. The quality of these records is dependent on a careful calibration of the transfer functions used to translate the sedimentary lipid signals to the local environment. Here, we examine and calibrate the UK'37 and TEX86 lipid-based temperature proxies in 48 surface sediments and relate these to ambient sea surface temperatures and other environmental data. The UK'37-based temperatures in surface sediments reflect winter/spring sea surface temperatures in agreement with other studies demonstrating maximum haptophyte production during the colder season. The TEX86-based temperatures for the nearshore sites also reflect winter sea surface temperatures. However, at the most offshore sites, they correspond to summer sea surface temperatures. Additional lipid and environmental data including the distribution of the BIT index and remote-sensed chlorophyll-a suggest a shoreward increase of the impact of seasonal and spatial variability in nutrients and control of planktonic archaeal abundance by primary productivity, particle loading in surface waters and/or overprint by a cold-biased terrestrial TEX86 signal. As such the offshore TEX86 values seem to reflect a true summer signal to the effect that offshore UK'37 and TEX86 reconstruct winter and summer temperature, respectively, and hence provide information on the annual temperature amplitude.

Magnetic and mineralogical analyses of Ganzi and Luochuan sections

We present a first combined environmental magnetic and geochemical investigation of a loess-paleosol sequence (<55 ka) from the Chuanxi Plateau on the eastern margin of the Tibetan Plateau. Detailed comparison between the Ganzi section and the Luochuan section from the Chinese Loess Plateau (CLP) allows quantification of the effects of provenance and climate on pedogenic magnetic enhancement in Chinese loess. Rare earth element patterns and clay mineral compositions indicate that the Ganzi loess originates from the interior of the Tibetan Plateau. The different Ganzi and CLP loess provenances add complexity to interpretation of magnetic parameters in terms of the concentration and grain size of eolian magnetic minerals. Enhanced paleosol magnetism via pedogenic formation of ferrimagnetic nanoparticles is observed in both sections, but weaker ferrimagnetic contributions, finer superparamagnetic (SP) particles and stronger chemical weathering are found in the Ganzi loess, which indicates the action of multiple pedogenic processes that are dominated by the combined effects of mean annual precipitation (MAP), potential evapotranspiration (PET), organic matter and aluminium content. Under relatively high MAP and low PET conditions, high soil moisture favours transformation of ferrimagnetic minerals to hematite, which results in a relatively higher concentration of hematite but weaker ferrimagnetism of Ganzi loess. Initial growth of superparamagnetic (SP) particles is also documented in the incipient loess at Ganzi, which directly reflects the dynamic formation of nano-sized pedogenic ferrimagnets. A humid pedogenic environment with more organic matter and higher Al content also helps to form finer SP particles. We therefore propose that soil water balance, rather than solely rainfall, dominates the type, concentration and grain size of secondary ferrimagnetic minerals produced by pedogenesis.