Techniques and Technologies for Countrymen, Case Study: Coca-Coffee Growers in Miranda (Cauca, Colombia)

En el presente artículo, resultado de investigación financiada por la Universidad del Cauca, se evalúan algunas acciones ambientales de comunidades campesinas cafeteras-cocaleras en Miranda, Cauca. Para tal fin, se realizó un diagnóstico rural participativo y un análisis econométricoa través de modelos de regresión multivariados estimados por Mínimos Cuadrados Ordinarios Robustos a la White. Los resultados muestran que, sobre el uso y control de agua y suelo en ambos cultivos, las acciones de aprovisionamiento de agua para agricultura dependen en mayor grado de las acciones de aislamiento de las fuentes de agua y del manejo de coberturas vegetales y orgánicas en el suelo. Las acciones de aprovechamiento arbóreo dependen en mayor grado de las acciones de reforestación y de las conexiones entre áreas boscosas en la finca. Las prácticas de los campesinos cafeteros-cocaleros son más sustentables y sostenibles que las de los campesinos cafeteros que no son cocaleros.In this article -the result of a research work sponsored by Universidad del Cauca- several environmental actions from rural coffee-coca growing communities are assessed in Miranda, Cauca. For this purpose, a participative rural evaluation and an econometric analysis were conducted through multivariate regression models estimated through the White’s Robust Ordinary Least Squares. Results show that, in relation to use and control of water and soil in both cultivations, water supply actions for agriculture mostly depend on isolation actions of water springs and handling of plant and organic areas in the soil. Tree exploitation actions mostly depend on reforestation actions and connections between forested areas at the farm. Coffee-coca growing countrymen’s practices are more sustainable than those of coffee growing countrymen who do not grow coca at all

Graphical Markov models: overview

We describe how graphical Markov models started to emerge in the last 40 years, based on three essential concepts that had been developed independently more than a century ago. Sequences of joint or single regressions and their regression graphs are singled out as being best suited for analyzing longitudinal data and for tracing developmental pathways. Interpretations are illustrated using two sets of data and some of the more recent, important results for sequences of regressions are summarized.Comment: 22 pages, 9 figure

Environmental changes and violent conflict

This letter reviews the scientific literature on whether and how environmental changes affect the risk of violent conflict. The available evidence from qualitative case studies indicates that environmental stress can contribute to violent conflict in some specific cases. Results from quantitative large-N studies, however, strongly suggest that we should be careful in drawing general conclusions. Those large-N studies that we regard as the most sophisticated ones obtain results that are not robust to alternative model specifications and, thus, have been debated. This suggests that environmental changes may, under specific circumstances, increase the risk of violent conflict, but not necessarily in a systematic way and unconditionally. Hence there is, to date, no scientific consensus on the impact of environmental changes on violent conflict. This letter also highlights the most important challenges for further research on the subject. One of the key issues is that the effects of environmental changes on violent conflict are likely to be contingent on a set of economic and political conditions that determine adaptation capacity. In the authors' view, the most important indirect effects are likely to lead from environmental changes via economic performance and migration to violent conflict. © 2012 IOP Publishing Ltd

Searching for phenotypic causal networks involving complex traits: an application to European quail

<p>Abstract</p> <p>Background</p> <p>Structural equation models (SEM) are used to model multiple traits and the casual links among them. The number of different causal structures that can be used to fit a SEM is typically very large, even when only a few traits are studied. In recent applications of SEM in quantitative genetics mixed model settings, causal structures were pre-selected based on prior beliefs alone. Alternatively, there are algorithms that search for structures that are compatible with the joint distribution of the data. However, such a search cannot be performed directly on the joint distribution of the phenotypes since causal relationships are possibly masked by genetic covariances. In this context, the application of the Inductive Causation (IC) algorithm to the joint distribution of phenotypes conditional to unobservable genetic effects has been proposed.</p> <p>Methods</p> <p>Here, we applied this approach to five traits in European quail: birth weight (BW), weight at 35 days of age (W35), age at first egg (AFE), average egg weight from 77 to 110 days of age (AEW), and number of eggs laid in the same period (NE). We have focused the discussion on the challenges and difficulties resulting from applying this method to field data. Statistical decisions regarding partial correlations were based on different Highest Posterior Density (HPD) interval contents and models based on the selected causal structures were compared using the Deviance Information Criterion (DIC). In addition, we used temporal information to perform additional edge orienting, overriding the algorithm output when necessary.</p> <p>Results</p> <p>As a result, the final causal structure consisted of two separated substructures: BW→AEW and W35→AFE→NE, where an arrow represents a direct effect. Comparison between a SEM with the selected structure and a Multiple Trait Animal Model using DIC indicated that the SEM is more plausible.</p> <p>Conclusions</p> <p>Coupling prior knowledge with the output provided by the IC algorithm allowed further learning regarding phenotypic causal structures when compared to standard mixed effects SEM applications.</p

The dollar-euro exchange rate and monetary fundamentals

Abstract This study analyses the relationship between the dollar-euro exchange rate and macroeconomic fundamentals according to the monetary model after 1999. Multivariate and time-varying univariate cointegration techniques are used to test for a long-run equilibrium and changes in the underlying coefficients. Our results provide clear evidence of a long-run relationship between exchange rates and fundamentals. However, we find significant changes in the economic impact of fundamentals on the dollar-euro exchange rate. Both long-run and the short-run coefficients are shown to be strongly time-varying and significantly affected by the financial crisis and the emergence of unconventional monetary policy