Validation of a Score for the Detection of Subjects with High Risk for Severe High-Altitude Illness.

PURPOSE: A decision tree based on a clinicophysiological score (severe high-altitude illness (SHAI) score) has been developed to detect subjects susceptible to SHAI. We aimed to validate this decision tree, to rationalize the prescription of acetazolamide (ACZ), and to specify the rule for a progressive acclimatization. METHODS: Data were obtained from 641 subjects in 15 European medical centers before and during a sojourn at high altitude. Depending on the value of the SHAI score, advice was given and ACZ was eventually prescribed. The outcome was the occurrence of SHAI at high altitude as a function of the SHAI score, ACZ prescription, and use and fulfillment of the acclimatization rule. RESULTS: The occurrence of SHAI was 22.6%, similar to what was observed 18 yr before (23.7%), whereas life-threatening forms of SHAI (high-altitude pulmonary and cerebral edema) were less frequent (2.6%-0.8%, P = 0.007). The negative predictive value of the decision tree based was 81%, suggesting that the procedure is efficient to detect subjects who will not suffer from SHAI, therefore limiting the use of ACZ. The maximal daily altitude gain that limits the occurrence of SHAI was established at 400 m. The occurrence of SHAI was reduced from 27% to 12% when the recommendations for ACZ use and 400-m daily altitude gain were respected (P < 0.001). CONCLUSIONS: This multicenter study confirmed the interest of the SHAI score in predicting the individual risk for SHAI. The conditions for an optimized acclimatization (400-m rule) were also specified, and we proposed a rational decision tree for the prescription of ACZ, adapted to each individual tolerance to hypoxia.

Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments.

The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.