Farmer Family Learning Groups for Community Development

The approach which is owned by everybody who uses it. Farmer Family Learning Groups are groups of farmer families, who together define their needs and goals in relation to their own future development, both as individuals, families and as a group - and then they help each other to reach the goals. The groups form strong networks and help each other, and help the entire local community. Organic farming is practiced in ways which are contextual specific and dependent on the environment, wherever it is being practiced. Forming farmer groups using this approach is likewise contextual specific and entirely determined by the needs and wishes and agreements within the group of families who in the first place decided to form a group for conscious and goal directed development in their households and local community. The beauty of the approach – like many other empowering group approaches – is its flexibility and at the same time strong and clear foundation on values like respect, trust, equality, common learning, building up human and social capital and knowledge which is relevant and meaningful to each participant and learner, as well as probably the most important value: ownership. Contact to the authors and further information can be obtained through the web pages of SATNET, NOGAMU and OD. Tell me and I will forget Show me and I will remember Involve me and I will understand Step back and I will ac

Taking our future in our hands - storytelling from Uganda to inspire action

This book aims at telling about our common learning in projects in different parts of Uganda, where farmer families and local communities have worked since 2009 to improve their livelihoods and environments based on common learning about organic and agroecological farming, joint efforts for better livelihoods and community development, using the approach of ‘Farmer Family Learning Groups’ (FFLG). This way of working together takes the ‘family approach’: our starting point is whole households working together on each their individual farm. As opposed to the methods where farmer groups work on demonstration farms or demonstration plots, this project is based on groups, which work at each other's individual farms, not focusing on one enterprise, but on building up their entire farming systems. The groups never followed the curricula of any manual directing ‘the one and only right approach’ – they learned together, set their own goals, and worked together in ways which were appropriate in the given geographical, climatic and social context

Landscape Control Of Thunderstorm Development In Interior Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2003General Circulation Models suggest a future climate of warmer and possibly drier summers in the boreal forest region, which could change fire regimes in high latitudes. Thunderstorm development is a dominant factor in the continental boreal forest fire regime, through its influence as a fire starting mechanism. Global Climate Change research has identified the land-atmosphere interface as a vital area of a needed research in order to improve our predictions of climate change. This dissertation has focused on the development of thunderstorms and lightning strike activity in a boreal forest region in Interior Alaska and on how the underlying surface can influence their development. I have examined the distributions and correlations between lightning strikes, thunderclouds, thunderstorm indices (CAPE and LI), elevation, and vegetation variables in Alaska. The relationships were examined at scales ranging from the Interior region of the state to individual wildfire burn scars, and at temporal scales ranging from the annual to daily. The objective is to understand the influential factors and processes responsible for thunderstorm development in Alaska, such that we may produce well-founded predictions on future thunderstorm regimes caused by a changing climate. The scale-related studies of this dissertation show that both processes and important variables for development of thunderstorms and lightning activity vary within and between the scales. It appears that on the larger scales, the combined effects of boreal forest and elevation on increased lightning strike activity were more prevalent than at the smallest scale (local). When the scale gets too small for the boundary layer to be affected (<10km), land surface effects on lightning cannot be. My results suggest that the underlying surface (in the form of areal forest coverage and vegetation) has more of an influence on convective development on days with airmass storms than on days with synoptic storms

Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use