A multidimensional farming system typology

Farm Management,

Potentials of a Harmonised Database for Agricultural Market Modelling

The study analysed existing databases for agricultural market data on errors and discrepancies and to elaborate the possibilities to harmonise datasets for policy modelling. The study supports DG AGRI in improving quality and timely availability of data for market modelling and ensuring that data from different sources are consistent. This study aims to provide a structure for a consolidated database for policy modelling which does not alter existing databases. Within this report, existing databases are analysed to derive key insights for setting-up a harmonised metabase. As available databases comprise statistical databases as well as scientific model databases, both groups are studied. For the purpose of this study, statistical databases are defined as providers of the information that international institutes receive from their reporters, while the reporters are required to provide harmonised, complete, consistent, and where possible, timely data series for establishing models or other quantitative methods. Nevertheless, a statistical database can also serve as a model database, such as e.g. PS&D. Statistical databases from international institutions (FAO, USDA, Eurostat), as well as model databases (AGLINK/COSIMO, AGMEMOD, CAPRI/CAPSIM, ESIM, FAPRI, GTAP, FARM, IMPACT), were studied to find ways of consolidating data and providing insights that allow for a better comparison of model results. For this reason, various classification schemes used in agricultural statistics were reviewed (country, product, balance item, year, unit), as was the manner in which the different modelling groups have dealt with these classifications in their databases. Besides a common classification, a harmonised database for market modelling purposes will require further efforts to be applied to a consolidation effort for the original data. Such a procedure must be supplemented by methods dealing with completion and balancing.JRC.J.5-Agriculture and Life Sciences in the Econom

Dynamics of food systems in Sub-Saharan Africa : Implications for consumption patterns and farmers’ position in food supply chains

This paper looks into the dynamics in the food system in SSA countries, describing developments in drivers of the food system, analysing food consumption patterns in selected SSA countries, investigating the pace of change of the regional food retail formats and the impacts it has on how local production is connected to modern food retailers. More specifically, the research objectives are to: Depict the trends in population growth, urbanisation rates, income growth and the food-system environment as drivers of change in dietary patterns; Investigate the trends in food consumption patterns in a range of SSA countries, with attention to differences between urban and rural consumption trends; Illustrate the changing food retail and provisioning system in the SSA region with examples and data from selected countries; Analyse the effects of modernising food systems on small farmers’ position in local and regional supply chains and explore whether dynamics in the SSA food retail structure and consumption patterns have had implications for food import dependency in certain countries in the region

We are what we eat: An economic tool for tracing the origins of nutrients with entry points for action

We develop a methodology for incorporating nutrition impacts in economy-wide analyses, providing entry points for where, when and how to act. It accounts for three channels of consumption, directly via primary commodities and indirectly via processed foods and food-related services, and produces indicators showing content by nutrient (currently calories, proteins, fats and carbohydrates), channel, source region and sector. The paper applies the framework in a CGE model (MAGNET) and uses FAO data to project nutritional outcomes resulting from the global food system over time. The analysis confirms that developing regions catch up with developed regions, with the USA at the high-end of nutrient consumption, whilst Southern Africa lags behind. In the USA the processed food channel dominates, whereas in Southern Africa the direct channel dominates. In the USA, and similar regions, fat taxes (thin subsidies) on unhealthy (healthy) processed foods, technologies reducing bad ingredients (e.g. trans fats, salt), improved food labelling, information and marketing campaigns, and/or targeted cash transfers may be worthwhile to investigate. In Southern Africa, and regions alike, technological advances increasing nutrient availability via primary agriculture and/or cash transfers enabling access may be more pertinent. The relative fixedness of sectoral origins shows that consumption habits change slowly and are visible only in the long term. For certain regions, including Southern Africa and USA, nutrient import dependency increases with substantial variations in regional sourcing. This implies that concerted action across the globe is crucial to reach diet, nutrition and health goals, and should include upcoming Asian economies, Africa (excl. Southern Africa) and the Middle East. Heterogeneity of results necessitates future ex-ante quantitative policy analyses on a more detailed and context-specific basis

Energy Availability and Nutritional Intake during Different Training Phases of Wheelchair Athletes.

Optimizing nutritional intake and timing helps athletes to improve performance and long-term health. Different training phases can require varying nutritional needs. In this study, we conducted a descriptive assessment of dietary intake, energy availability (EA), and blood biochemical parameters in elite wheelchair athletes during distinct training phases. Data analyzed in this study were collected as part of a randomized controlled crossover trial exploring the feasibility of probiotics and prebiotic supplementation. Data were obtained from consecutive three-day diaries and blood samples, both collected at four different time points across four consecutive months. We included 14 athletes (mean (standard deviation) age 34 (9) years, eight females, and six males) active in different wheelchair sports. The mean daily nutritional intake (g/kg body mass) for females and males was 2.7 (0.9) and 4.0 (0.7) for carbohydrates, 1.1 (0.3) and 1.5 (0.3) for protein, and 0.8 (0.3) and 1.4 (0.2) for fat. EA did not change across the four time points in either female (p = 0.30) or male (p = 0.05) athletes. The mean EA was lower in female athletes compared to male athletes (p = 0.03). Low EA (≤30 kcal/ kg fat-free mass/day) was observed in female (58 (29) % of days) and male (34 (23) % of days) athletes. Iron deficiency with anemia was observed in two female athletes. Mean vitamin D levels were insufficient (<75 nmol/L). Macronutrient intake, EA, and blood biochemical parameters were suboptimal in this cohort of elite wheelchair athletes, especially in female athletes

Automatic detection of microsleep episodes with deep learning

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30-s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e. with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. Code of algorithms ( https://github.com/alexander-malafeev/microsleep-detection ) and data ( https://zenodo.org/record/3251716 ) are available