Comparing methods for summarizing a training load in prediction models of swimming performance

International audienceIntroduction. Training quantifications are valuable for monitoring and prescribing elite swimmers’ training and are indispensable in mathematical models that attempt to accurately predict performance. Modelling the association of training with performance raises an important issue: how should we account for volumes at different training intensities? Mujika et al. (1996) constructed a training load by adding weighted (by a priori constants representing energetic intensities) volumes from each intensity. Avalos et al. (2003) computed a training load as the sum of normalised training intensities. Here we compared the predictive accuracy of these methods to others based on: a/ alternative normalisations, b/ summary scores derived from data, and c/ machine learning techniques, with recognised predictive qualities, such as PLS.Methods. Training volumes at eight intensity levels (in kilometres and minutes per week, for in-water and dry-land workouts, respectively) and performances in competition of 138 professional French swimmers were collected during 20 seasons. Training intensities were determined using measurements of blood lactate concentrations. We assumed that swimmers may react differently to the same training and over time, thus we used mixed-effects models adjusted for sex, age, swimming distance and event specialty. The comparison criterion was the cross-validated prediction error.Results. Summary scores for three training loads (low-intensity/high-intensity/dry-land workouts) with data derived weights showed the best results (mean cross-validated prediction error ± SD were 0.60±0.89, 0.50±0.62 and 0.10±0.19 for sprint, mid- and long-distances, respectively). However, cross-validated prediction errors were close relative to their variances, which were high.Conclusions. The use of complex machine learning techniques did not lead to more accuracy in predicting performance. Although data derived scores showed the lowest prediction error, the statistical variability was too high for being conclusive. A possible explanation is that the lactate sensitivity to extraneous factors (mode of exercise, technique quality of training, diet or sleep quality prior to test) and the subject-specific variations in lactate thresholds introduce not negligible measurement error. As practical recommendation, we suggest completing lactate measurements with athlete/coach questionnaires to better assess the physiological stress associated with the training load. Also, errors-in-variables models might be more appropriated

Modelling of optimal training load patterns during the 11 weeks preceding major competition in elite swimmers

International audiencePeriodization of swim training in the final training phases prior to competition and its effect on performance have been poorly described. We modeled the relationships between the final 11 weeks of training and competition performance in 138 elite sprint, middle-distance, and long-distance swimmers over 20 competitive seasons. Total training load (TTL), strength training (ST), and low- to medium-intensity and high-intensity training variables were monitored. Training loads were scaled as a percentage of the maximal volume measured at each intensity level. Four training periods (meso-cycles) were defined: the taper (weeks 1 to 2 before competition), short-term (weeks 3 to 5), medium-term (weeks 6 to 8), and long-term (weeks 9 to 11). Mixed-effects models were used to analyze the association between training loads in each training meso-cycle and end-of-season major competition performance. For sprinters, a 10% increase between ∼20% and 70% of the TTL in medium- and long-term meso-cycles was associated with 0.07 s and 0.20 s faster performance in the 50 m and 100 m events, respectively (p < 0.01). For middle-distance swimmers, a higher TTL in short-, medium-, and long-term training yielded faster competition performance (e.g., a 10% increase in TTL was associated with improvements of 0.1-1.0 s in 200 m events and 0.3-1.6 s in 400 m freestyle, p < 0.01). For sprinters, a 60%-70% maximal ST load 6-8 weeks before competition induced the largest positive effects on performance (p < 0.01). An increase in TTL during the medium- and long-term preparation (6-11 weeks to competition) was associated with improved performance. Periodization plans should be adapted to the specialty of swimmers.En natation, la périodisation des phases d’entraînement précédant les compétitions majeures a été peu décrite. Nous avons modélisé les relations entre les 11 dernières semaines d’entraînement et les performances en compétition chez 138 nageurs élites. La charge d’entraînement totale (TTL), l’entraînement de force (ST), les variables d’entraînement de basse à moyenne et haute intensité ont été quantifiées. Les charges d’entraînement ont été normalisées en pourcentage du volume maximal individuel pour chaque niveau d’intensité. Quatre périodes d’entraînement (méso-cycles) ont été définies, l’affutage (semaines 1 à 2 avant la compétition), à court-terme (semaines 3 à 5), moyen-terme (semaines 6 à 8) et long-terme (semaines 9 à 11). Les modèles à effets mixtes ont été utilisés pour analyser les associations entre les charges d’entraînement dans chaque méso-cycle et la performance de fin de saison. Pour les sprinters, chaque augmentation de TTL de 10 % entre ∼20–70 % dans les méso-cycles à long et moyen-terme a été associée avec des performances plus rapides de 0,07 s et 0,20 s dans les épreuves de 50 m et 100 m (p < 0,01). Pour les nageurs de demi-fond une TTL plus élevée à court, moyen et long terme a induit des performances en compétition plus rapides (chaque augmentation de TTL de 10 % entre ∼20–70 % a été associée à des performances plus rapides de 0,3–1,6 s au 400 m, p < 0,01). Pour les sprinters, une ST entre 60–70 % 6–8 semaines avant la compétition a induit les effets positifs les plus élevés (p < 0,01). TTL 6–11 semaines avant la compétition a amélioré les performances en compétition

Un élargissement des critères d'évaluation de la performance économique pour rendre compte de la performance économique globale des exploitations agricoles. Cadre théorique et application avec la méthode IDEA version 4

National audienceFarmers are now faced with an increase in risks and uncertainties in view of the global changes affecting their activities: multiplicity of sometimes conflicting societal issues, divergent interests of the different stakeholders, climate change, deregulation of the Commun Agricultural Policy, restrictions of use on certain pesticides and transmission / recovery of their farms. In this context of a transition to new models of agricultural development under construction, economic evaluation but also strategic management of farms involves revisiting the traditional reading of economic performance to take into account the multi-dimensionality of these challenges. This communication proposes an enlarged model for assessing the economic performance of farms in terms of the sustainability criteria of agriculture. The proposed model reports on the overall economic performance of an agricultural operation defined as its degree of achievement or level of economic sustainability. The construction of this model mobilized two types of work. On the one hand, it is based on the results of a broad analysis of the literature on the evaluation of economic performance in agriculture (its approaches, its disciplinary currents, its various associated evaluation methods, its performance benchmarks). On the other hand, theoretically, it is directly derived from the conceptual framework for evaluating sustainability in agriculture developed in the work on the new version four of the Indicators of Sustainability of Farms (IDEA version 4). This new framework combines two types of Indicator-Based Conceptual Frameworks : one based on 12 sustainable agriculture goals and one based on a systemic approach focusing on 5 properties of sustainable agricultural systems (Autonomy, Robustness, Ability to produce and reproduce goods and services, Territorial embeddedness, and Overall responsibility). This conceptual framework is the basis of the theoretical choices of the eleven economic indicators constituting the presented model corresponding to the evaluative grid of the economic dimension of the IDEA version 4 method. At the operational level, this model of evaluation of the overall economic performance is a hierarchical combination of 11 indicators structured into four components (economic and financial sustainability, independence, transferability and overall efficiency). It assigns, for a farm, an economic sustainability score or overall economic performance rating on a scale of 0 to 100 sustainability units. The application of this global economic performance model, illustrated from three case studies, shows that it is likely to analyze the overall economic performance for different uses at three different scales: (i) the agricultural perspective of individual consulting, (ii) in group dynamics to identify the margins of progress, differences between farms with the same agroecological practices and finally (iii) to characterize the overall economic performance of the main technical and economic orientations of the Farm France by calculating eight of the eleven indicators on data from the Farm Accountancy Data Network.Les agriculteurs sont aujourd'hui confrontés à un accroissement des risques et incertitudes compte tenu des changements globaux affectant leurs activités : multiplicité d'enjeux sociétaux parfois contradictoires, intérêts divergents des différentes parties prenantes, changement climatique, dérégulation de la PAC, restrictions d'usages sur certains pesticides et transmission/reprise de leurs exploitations. Dans ce contexte d'une transition vers de nouveaux modèles de développement agricole en cours de construction, l'évaluation économique mais aussi le pilotage stratégique des exploitations agricoles implique de revisiter la lecture traditionnelle de la performance économique pour prendre en compte la multi-dimensionnalité de ces défis. La présente communication propose un modèle élargi d'évaluation de la performance économique des exploitations agricoles à l'aune des critères de la durabilité de l'agriculture. Le modèle proposé rend compte de la performance économique globale d'une exploitation agricole définie comme son degré d'atteinte ou niveau de durabilité économique. La construction de ce modèle a mobilisé deux types de travaux. D'une part, il s'appuie sur les résultats d'une large analyse de la littérature sur l'évaluation de la performance économique en agriculture (ses approches, ses courants disciplinaires, ses différentes méthodes d'évaluation associées, ses référentiels de performance). D'autre part, au plan théorique, il est directement issu du cadre conceptuel d'évaluation de la durabilité en agriculture développés dans les travaux sur la nouvelle version quatre de la méthode Indicateurs de Durabilité des Exploitations Agricoles (IDEA version 4). Ce nouveau cadre conceptuel combine une double approche évaluative de la durabilité en agriculture basée sur douze objectifs assignés à une agriculture durable et cinq propriétés des systèmes agricoles durables. Il est à la base des choix théoriques des onze indicateurs économiques constitutifs du modèle présenté correspondant à la grille évaluative de la dimension économique de la méthode IDEA version 4. Au plan instrumental, ce modèle d'évaluation de la performance économique globale est une combinaison hiérarchisée de onze indicateurs structurés en quatre composantes (viabilité économique et financière, indépendance, transmissibilité et efficience globale). Il attribue, pour une exploitation agricole, un score de durabilité économique ou note de performance économique globale sur une échelle de 0 à 100 unités de durabilité. L'application de ce modèle de performance économique globale, illustrée à partir de trois études de cas, montre qu'il est susceptible d'analyser la performance économique globale pour différents usages à trois échelles différentes : (i) l'exploitation agricole dans une perspective d'un conseil individuel, (ii) en dynamique de groupe pour identifier les marges de progrès, différences entre exploitations ayant les mêmes pratiques agroécologiques et enfin (iii) pour caractériser la performance économique globale des grandes orientations technico-économiques de la Ferme France en calculant huit des onze indicateurs sur des données du Réseau d'Information Comptable Agricole

IJARGE

Given the challenge of prejudging whether contracting a MAEt - a territorial agri-environmental policy in France - constitutes an environmental effort on the part of the farmers, this study addresses questions of fairness raised by MAEt. Although the policy is focused on improving environment quality, there are consequences in terms of equity of access and the level of compensation obtained. The study employed statistics over the period of 2007-2013 to identify farms with access to MAEt and the associated inequalities. Contracting farmers are similar to those receiving other direct subsidies; thus, the same equity issues are addressed. However, monetary compensation does not appear to worsen income inequality between farmers. Finally, a focus on MAEt implementation in three regions highlights the key role of contrasted but persistent logics in the definition of priority areas, a hint at a possible lack of realignment of the MAEt scheme after 2013