Les facteurs de l’adoption des nouvelles technologies en agriculture en Afrique Subsaharienne: une revue de la littérature

L’Afrique Subsaharienne n'a pas assez bénéficié des grandes révolutions connues du monde agricole qui ont permis d’accroitre les productivités. Malgré l’existence des nouvelles technologies, les niveaux des productivités agricoles demeurent faibles et inférieurs à ceux d’autres régions en développement. Un certain nombre de facteurs, dont les contraintes à l’adoption des nouvelles technologies, pourraient expliquer ces faibles productivités. En effet, l’adoption des nouvelles technologies en agriculture en Afrique Subsaharienne connait l'influence de plusieurs facteurs dont les caractéristiques socioéconomiques des ménages, le mode de fonctionnement et de gestion des productions, les pratiques marchandes, les caractéristiques de transformation, le degré de sensibilisation et les réseaux sociaux. La prise en compte de ces facteurs est primordiale pour la réussite des nouveaux programmes et projets d’introduction et de diffusion des nouvelles technologies. Ce qui contribuerait à accroître les productivités et réduire la pauvreté et l’insécurité alimentaire à l’échelle du continent. / / / Sub-Saharan Africa has not benefited sufficiently from the great revolutions in the agricultural world that have made it possible to increase productivity. Despite the existence of new technologies, levels of agricultural productivity remain low, and lower than those of other developing regions. A number of factors, including constraints on the adoption of new technologies, could explain this low productivity. Indeed, the adoption of new technologies in agriculture in Sub-Saharan Africa is influenced by several factors, including the socio-economic characteristics of households, the mode of operation and management of production, market practices, processing characteristics, and the degree of awareness and social networks. It is essential to consider these factors if new programmes and projects for introducing and disseminating new technologies are to be successful. This would help increase productivity, and thereby reduce poverty and food insecurity across the continent

Economic benefits of extending the grazing season in beef cattle production in Atlantic Canada

Today, feeding cost is a significant issue for the economic viability of livestock operations, including beef production. The aim of this study was to determine, in comparison to the conventional feeding approach, the advantages and expected value of extending the grazing season in Atlantic beef production using stockpiled and baled forage. The research methodology is based on the partial budgeting approach. The study shows that extending the grazing season can reduce by 54% the total annual production cost for feed, yardage and straw bedding. Indeed, this innovative feeding approach can contribute to avoiding expenses of $7,331.92 per farm per year through eliminating and/or reducing overwintering costs for feed (16$0.92 of the overwintering production costs per cow/calf pair per day. Moreover, extending the grazing season does not seem to compromise animals’ performance. This practice could therefore be an alternative solution to enhance beef farm financial viability and can also contribute to the sustainable development of beef farms through other services provided such as recreation functions and environmental protection. These results reflect the necessity of supporting and promoting the adoption of extended grazing season practices in Atlantic beef production

Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study

Time is a crucial parameter in the assessment of comorbidities in population-based studies, as it permits to identify more complex disease patterns apart from the pairwise disease associations. So far, it has been, either, completely ignored or only, taken into account by assessing the temporal directionality of identified comorbidity pairs. In this work, a novel time-analysis framework is presented for large-scale comorbidity studies. The disease-history vectors of patients of a regional Spanish health dataset are represented as time sequences of ordered disease diagnoses. Statistically significant pairwise disease associations are identified and their temporal directionality is assessed. Subsequently, an unsupervised clustering algorithm, based on Dynamic Time Warping, is applied on the common disease trajectories in order to group them according to the temporal patterns that they share. The proposed methodology for the temporal assessment of such trajectories could serve as the preliminary basis of a disease prediction system.We received support from ISCIII-FEDER (PI13/00082, CP10/00524, CPII16/00026), IMI-JU under grants agreements no. 115372 (EMIF), resources composed of financial contribution from the EU-FP7 (FP7/2007-2013) and EFPIA companies in kind contribution, and the EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics) and no. 676559 (Elixir-Excelerate). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D + i 2013-2016, funded by ISCIII and FEDER. Funding has been also received by the Marie-Curie UPFellowship Program