Uneven development and the governance of agricultural commodity booms : the case of soybean in South America

Issues related to food security have long been closely tied to the dynamics of the global political economy. The latest price peak experienced in the commodities market (2007-2008) greatly affected agricultural commodities, creating significant imbalances in production and consumption. This research develops an interdisciplinary approach that links together issues of natural resource governance, development, and transformations in the global political economy to explore the ways in which countries of South America govern commodity booms. In other words, this thesis examines how these global dynamics affect the ways in which food-producing states manage the wealth produced during commodity booms and how this is wealth is subsequently distributed among different sectors of society. In South America, the recent commodity boom has led to an expansion of primary production oriented towards export markets, creating imbalances in their domestic productive structures. This thesis focuses on the production and trade of soybean in three countries of the Southern Cone: Argentina, Brazil, and Paraguay. Following the boom, soybean production has come to dominate the agricultural sectors and overall exports of these countries, with some authors going as far as to dub this rapidly expanding industry the ‘Soybean Republic’. This research engages with cutting edge debates in International Political Economy, with a conceptual focus that draws from human geography and brings in space as both contingent and constituted by the changing productive and trade dynamics. By looking at the development of fixed infrastructure and dynamics of capital mobility, this research explores the patterns of uneven development that emerge from the expansion of the soybean complex, as well as the capacity of the Argentine, Brazilian, and Paraguayan states to govern the distribution of the profits emanating from it

Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis

In this work, near infrared (NIR) spectroscopy and multivariate data analysis were investigated as a fast and non disruptive method to classify green coffee beans on continents and countries bases. FT-NIR spectra of 191 coffee samples, origin from 2 continents and 9 countries, were acquired by two different laboratories. Laboratory-independent Partial Least Square-Discriminant Analysis and interval PIS-DA models were developed by following a hierarchical approach, i.e. considering at first the continent and then the country of origin as discrimination rule. The best continent-based classification model was able to identify correctly more than 98% in prediction, whereas 100% of them were correctly predicted by the best country-based classification model. The inter-laboratory reliability of the proposed method was confirmed by McNemar test, since no significant differences (P > 0.05) were found. Furthermore, a validation was performed predicting the spectral test set of a laboratory using the model developed by the other one

The Euler-Maruyama approximation for the absorption time of the CEV diffusion

A standard convergence analysis of the simulation schemes for the hitting times of diffusions typically requires non-degeneracy of their coefficients on the boundary, which excludes the possibility of absorption. In this paper we consider the CEV diffusion from the mathematical finance and show how a weakly consistent approximation for the absorption time can be constructed, using the Euler-Maruyama scheme