Sustainable rural livelihoods to analyse family farming dynamics: A comparative perspective

The very nature of family farming makes it a complex scientific subject, being at the same time a social form of production and an economic agent. Its nature challenges disciplines that most of the time overlook dimensions that do not fit in with their own framework leading to partial views in anthropology, sociology, political science or economics, just to mention the most common disciplinary focus on rural societies. We suggest exploring the well-known Sustainable Rural Livelihood framework as a comprehensive and open conceptual design to address the evolution of family farming. While the entry point concerns individuals, it also considers the social structures and institutions in which they are embedded. It also contemplates natural, social and human assets in addition to physical and financial ones. The activity system developed by each individual within its social setting goes beyond sectorial approaches; the strategies developed are contextualized and influenced by policies. To illustrate how this framework can be implemented, we developed a case study approach in contrasting rural contexts ranging from Argentina, Brazil or Nicaragua for Latin American situations, to Indonesia, China or India for Asia, or to Mali, Cameroon or Mozambique for African illustrations. The cases will not be extensively presented here as we choose to highlight some of the main findings and crosscutting themes as ways and means of adapting to changing contexts. We also discuss the challenges and perspectives faced by family farming from other forms of production and provide some insight into "blind" issues: the social drawbacks and political dimensions linked to agriculture related to broader territorial and national concerns

An Analytical Expression for the Hubble diagram of supernovae and gamma-ray bursts

A recent paper by Harmut Traunm\"uller shows that the most adequate equation to interpret the observations on magnitude and redshift from 892 type 1a supernovae would be mu = 5 log[(1+z) ln(1+z)] + const. We discuss this result which is exacly the one we have obtained few years ago when postulating a relation between the speed of light and the expansion of the universe. We also compare our analytical result to the conclusion of Marosi who studied 280 supernovae and gamma-ray bursts in the range 0.1014 < z < 8.1. The difference between his results and ours is at worst of 0.3 %.Comment: 7 pages, 1 figur

Looking Forward to a General Theory on Population Aging

The main theories on population aging based on recent data on human longevity, life expectancy, morbidity changes, disability trends, and mortality decrease are presented and discussed within their own geographic, cultural, socioeconomic, and medical contexts. The complex interactions between all these components do not facilitate trend forecasting of aging population (healthy aging versus disability pandemic). In the context of population aging, four elements were introduced with their implications: 1) an increase in the survival rates of sick persons, which would explain the expansion of morbidity, 2) a control of the progression of chronic diseases, which would explain a subtle equilibrium between the decrease in mortality and the increase in disability, 3) an improvement of the health status and health behaviors of new cohorts of elderly people, which would explain the compression of morbidity, and eventually 4) an emergence of very old and frail populations, which would explain a new expansion of morbidity. Obviously, all these elements coexist today, and future trend scenarios—expansion or compression of disability—depend on their respective weights leading to the need of elaborating "a general theory on population aging.” This theory has to be based on a world harmonization of functional decline measurements and a periodic "International Aging Survey” to monitor global aging through a sample of carefully selected countrie

Efficient learning in ABC algorithms

Approximate Bayesian Computation has been successfully used in population genetics to bypass the calculation of the likelihood. These methods provide accurate estimates of the posterior distribution by comparing the observed dataset to a sample of datasets simulated from the model. Although parallelization is easily achieved, computation times for ensuring a suitable approximation quality of the posterior distribution are still high. To alleviate the computational burden, we propose an adaptive, sequential algorithm that runs faster than other ABC algorithms but maintains accuracy of the approximation. This proposal relies on the sequential Monte Carlo sampler of Del Moral et al. (2012) but is calibrated to reduce the number of simulations from the model. The paper concludes with numerical experiments on a toy example and on a population genetic study of Apis mellifera, where our algorithm was shown to be faster than traditional ABC schemes