Physiological responses to repeated bouts of high-intensity ultraendurance cycling - a field study case report

The present study aimed to 1) examine the relationship between laboratory-based measures and high-intensity ultraendurance (HIU) performance during an intermittent 24-h relay ultraendurance mountain bike race (similar to20 min cycling, similar to60min recovery), and 2) examine physiological and performance based changes throughout the HIU event. Prior to the HIU event, four highly-trained male cyclists (age = 24.0 +/- 2.1 yr; mass = 75.0 +/- 2.7 kg; (V)over dot O-2peak = 70 +/- 3 ml.kg(-1).min(-1)) performed 1) a progressive exercise test to determine peak Volume of oxygen uptake ((V)over dot O-2peak), peak power output (PPO), and ventilatory threshold (T-vent), 2) time-to-fatigue tests at 100% (TF100) and 150% of PPO (TF150), and 3) a laboratory simulated 40-km time trial (TT40). Blood lactate (Lac(-)), haematocrit and haemoglobin were measured at 6-h intervals throughout the HIU event, while heart rate (HR) was recorded continuously. Intermittent HIU performance, performance HR, recovery HR, and Lac declined (P < 0.05), while plasma volume expanded (P < 0.05) during the HIU event. TF100 was related to the decline in lap time (r = -0.96; P < 0.05), and a trend (P = 0.081) was found between TF150 and average intermittent HIU speed (r = 0.92). However, other measures (V)over dot O-2peak, PPO, T-vent, and TT40) were not related to HIU performance. Measures of high-intensity endurance performance (TF100, TF150) were better predictors of intermittent HIU performance than traditional laboratory-based measures of aerobic capacity.

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts. Copyright (K) 2003 Royal Meteorological Society.