Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO 2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop-and regionspecific adaptation strategies to ensure food security for an increasing world population.climate change impact | global food security | major food crops | temperature increase | yield C rops are sensitive to climate change, including changes in temperature and precipitation, and to rising atmospheric CO 2 concentration (1, 2). Among the changes, temperature increase has the most likely negative impact on crop yields (3, 4), and regional temperature changes can be projected from climate models with more certainty than precipitation. Meteorological records show that mean annual temperatures over areas where wheat, rice, maize, and soybean are grown have increased by ∼1°C during the last century (Fig. 1A) and are expected to continue to increase over the next century (Fig. 1B) -more so if greenhouse gas emissions continue to increase. It is thus necessary to quantify the impact of temperature increase on global crop yields, including any spatial variations, to first assess the risk to world food security, and then to develop targeted adaptive strategies to feed a burgeoning world population (5).Several methods have been developed to assess the impact of temperature increase on crop yields (6). Process-based crop models characterize crop growth and development in daily time steps and can be used to simulate the temperature response of yield either in areas around the globe defined by grids or at selected field sites or points (1, 7). A third method, statistical modeling, uses observed regional yields and historical weather records to fit regression functions to predict crop responses (8,9). A fourth method is to artificially warm crops under nearnatural field conditions to directly measure the impact of increased Significance Agricultural production is vulnerable to climate change. Understanding climate change, especially the temperature impacts, is...

Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm.

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 CO 2 concentration [CO 2 ]. 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 [CO 2 ] and temperature.

This is a repository copy of Similar estimates of temperature impacts on global wheat yield by three independent methods.