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Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.

Drought risk is considered to be among the main limiting factors for maize (Zea mays L.) production in the Northeast Farming Region of China (NFR). Maize yield data from 44 stations over the period 1961–2010 were combined with data from weather stations to evaluate the effects of climatic factors, drought risk and irrigation requirement on rain-fed maize yield in specific maize growth phases. The maize growing season was divided into four growth phases comprising seeding, vegetative, flowering and maturity based on observations of phenological data from 1981 to 2010. The dual crop coefficient was used to calculate crop evapotranspiration and soil water balance during the maize growing season. The effects of mean temperature, solar radiation, effective rainfall, water deficit, drought stress days, actual crop evapotranspiration and irrigation requirement in different growth phases were included in the statistical model to predict maize yield. During the period 1961–2010, mean temperature increased significantly in all growth phases in NFR, while solar radiation decreased significantly in southern NFR in the seeding, vegetative and flowering phases. Effective rainfall increased in the seeding and vegetative phases, reducing water deficit over the period, whereas decreasing effective rainfall over time in the flowering and maturity phases enhanced water deficit. An increase in days with drought stress was concentrated in western NFR, with larger volumes of irrigation needed to compensate for increased dryness. The present results indicate that higher mean temperature in the seeding and maturity phases was beneficial for maize yield, whereas excessive rainfall would damage maize yield, in particular in the seeding and flowering phases. Drought stress in any growth stage was found to reduce maize yield and water deficit was slightly better than other indicators of drought stress for explaining yield variability. The effect of drought stress was particularly strong in the seeding and flowering phases, indicating that these periods should be given priority for irrigation. The yield-reducing effects of both drought and intense rainfall illustrate the importance of further development of irrigation and drainage systems for ensuring the stability of maize production in NFR.

The temporal change in soil organic carbon (SOC) was analysed over an 80-year period based on climate change predictions of four regional circulation models under the International Panel on Climate Change (IPCC) A1B emission scenario in the 21st century. A 20-year (1991–2010) set of observed climate data was used to form the baseline, and generate synthetic data for future scenario analyses. With increasing carbon dioxide (CO2) levels, and under continuous winter wheat production with conventional tillage at different nitrogen (N) input rates, three crop-soil models were used to study the temporal changes of SOC. Results indicated that soil carbon (C) generally decreased over the simulation period. In addition, increased N losses through leaching and denitrification were estimated. Decline in soil C under continuous mono-cropping systems indicated increased focus on N fertilization strategies. The results also suggested significant interactive effect of N input rate and climate variables on soil C and denitrification in response to climate change. The uncertainty was addressed by including the crop-soil models in a mixed-effect analysis so that the contribution of the models to the total variance of random variation was quantified. Statistical analysis showed that the crop-soil models are the main source for uncertainty in analysing soil C and N responses to climate change.

The response of grain yield, grain nitrogen (N), phenological development and evapotranspiration of winter wheat to climate change was analysed over an 80-year period based on climate change predictions of four regional circulation models (RCMs) under the IPCC (International Panel on Climate Change) A1B emission scenario for the 21st century using three process-based models; A 20-year set (1991–2010) of observed daily climate data from Aarslev, Denmark was used to form the baseline, from which the RCM data were generated. The simulation of crop growth was performed with increasing carbon dioxide (CO2) levels and under continuous mono-cropping system at different N input rates. Results indicated that grain yield and grain N will be reduced in the future despite increased CO2 concentration in the atmosphere. While the increased N input may increase yield, it will not increase grain N. The present study suggested that in Denmark, alternative strategies for organic N acquisition of plants must be developed. Statistical analyses showed that while the crop models were the main source of uncertainty in estimating crop performance indicators in response to climate change, the choice of RCM was the main source of uncertainty in relation to baseline estimations.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.

Published online 22 December 2014

BackgroundRight ventricular (RV) dysfunction is a strong predictor of poor outcomes of patients with mitral stenosis (MS). The aim of this study was to detect a subclinical right ventricular dysfunction in patients with MS.MethodsWe conducted a prospective study from January 2015 to June 2019 in 104 asymptomatic patients with MS (mean age: 46.1 ± 4.27 years), and compared to a group of 52 age and sex matched healthy subjects. Standard and speckle tracking echocardiography were performed; parameters of RV function were measured and compared. ResultsAlthough they were in normal range, RV fractional area change (RVFAC), tricuspid annular plane systolic excursion (TAPSE), tissue Doppler and pulsed wave Doppler RV myocardial performance index (MPI) and peak systolic velocity (S’) were decreased in patients with MS (p < 0.05). The global RV longitudinal strain (LSGRV) and the longitudinal strain of the free wall of the right ventricular (LSFRV) were lower in patients with MS (p, respectively, at 0.001 and < 0.001), 53.5% (n = 47) of patients had LSFRV < − 20%. A significant difference was noted between LSFRV and 2D strain of the septal wall (p = 0.002). No difference was found between patients with severe MS and moderate MS regarding LSFRV and LSGRV. No correlation between LSFRV and FAC (p = 0.85), MPI (p = 0.62), TAPSE (p = 0.31) and S’ (p = 0.78) was found.ConclusionOur study showed the presence of subclinical systolic dysfunction of the right ventricle in patients with MS.

Background The conventional preanesthetic evaluation process can be resource-intensive and time-consuming. Preanesthetic questionnaires have been proposed to alleviate the burden on clinical resources. This study aimed to assess the reliability and predictive accuracy of a pediatric preanesthetic screening questionnaire as a potential alternative to the standard preanesthetic consultation. Methods An evaluative, prospective, cross-sectional, single-center study was conducted from October 2022 to May 2023 in the Outpatient Clinic of the Department of Anesthesiology, Habib Thameur Hospital, Tunis. A 43-item bilingual (French/Arabic) paper-based questionnaire was completed for 300 pediatric patients (aged 1 month to 18 years) scheduled for elective surgery. Responses were compared with standard preanesthetic evaluations by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen’s kappa coefficient (κ) for agreement. Results All 300 patients completed the questionnaire (completion rate = 100%). Most were classified as ASA Physical Status I (78.3%). Agreement between questionnaire responses and physician assessments was excellent for 34 of 43 items (κ > 0.8; p < 0.001), with perfect agreement (κ = 1.000; p < 0.001) for item 10. Near-perfect agreement was observed for ASA classification (κ = 0.953; p < 0.001; concordance rate = 98.3%). Eight items had κ values between 0.397 and 0.798, and one item (myopathy) could not be analyzed due to uniformly negative responses. Conclusion The pediatric preanesthesia questionnaire demonstrated high reliability and strong agreement with physician assessments. It may serve as an effective triage tool to optimize outpatient workflows and reduce clinical workload, particularly for low-risk pediatric patients.