Effects of Landscape Segregation on Livelihood Vulnerability: Moving From Extensive Shifting Cultivation to Rotational Agriculture and Natural Forests in Northern Laos

This study investigates four decades of socio-economic and environmental change in a shifting cultivation landscape in the northern uplands of Laos. Historical changes in land cover and land use were analyzed using a chronological series of remote sensing data. Impacts of landscape change on local livelihoods were investigated in seven villages through interviews with various stakeholders. The study reveals that the complex mosaics of agriculture and forest patches observed in the study area have long constituted key assets for the resilience of local livelihood systems in the face of environmental and socio-economic risks. However, over the past 20 years, a process of segregating agricultural and forest spaces has increased the vulnerability of local land users. This process is a direct outcome of policies aimed at increasing national forest cover, eradicating shifting cultivation and fostering the emergence of more intensive and commercial agricultural practices. We argue that agriculture-forest segregation should be buffered in such a way that a diversity of livelihood opportunities and economic development pathways can be maintained.

Estimating the number of motor units using random sums with independently thinned terms

The problem of estimating the numbers of motor units N in a muscle is embedded in a general stochastic model using the notion of thinning from point process theory. In the paper a new moment type estimator for the numbers of motor units in a muscle is denned, which is derived using random sums with independently thinned terms. Asymptotic normality of the estimator is shown and its practical value is demonstrated with bootstrap and approximative confidence intervals for a data set from a 31-year-old healthy right-handed, female volunteer. Moreover simulation results are presented and Monte-Carlo based quantiles, means, and variances are calculated for N in{300,600,1000}.