Suchergebnisse

A documentary account of the Chernobyl disaster of April 1986, this is based on interviews with many of the participants. Shcherbak considers Chernobyl to be the most important event in the USSR since World War II and felt compelled to go and live there and interview those involved.

Comments This article is a U.S. government work, and is not subject to copyright in the United States. Abstract Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly 0.5 Mg ha 1 per °C. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

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. Process-based modelling of crop growth is an effective way of representing how crop genotype, environment and management interactions affect crop production to aid tactical and strategic decision making1. Process-based crop models are increasingly used to project the impact of climate change on crop yield2. However, current models produce different results, creating large uncertainty in crop yield simulations3. A model inter-comparison study within the Agricultural Model Inter-comparison and Improvement Project (AgMIP)4 of 29 widely used wheat models against field experimental data revealed that there is more uncertainty in simulating grain yields from the different models than from 16 different climate change scenarios3. The greatest uncertainty was in modelling crop responses to temperature3,5. Similar results were found with rice and maize crops6,7. Such uncertainty should be reduced before informing decision making in agriculture and government policy. Here, we show contrasting differences in temperature response functions of key physiological processes adopted in the 29 crop models. We reveal opportunities for improving simulation of temperature response in crop models to reduce the uncertainty in yield simulations. We aim to reassess the scientific assumptions underlying model algorithms and parameterization describing temperature-sensitive physiological processes, using wheat, one of the most important staple crops globally, as an example. We hypothesized that: (1) the difference among models in assumed temperature responses is the largest source of the uncertainty in simulated yields; and (2) the uncertainty in the multi-model ensemble results can be reduced by improving the science for modelling temperature response of physiological processes. Temperature affects crop performance primarily through its impact on (1) the rate of phenological development from seed germination to crop maturity, including the fulfilment of cold requirement (vernalisation); (2) the initiation and expansion of plant organs; (3) photosynthesis and respiration, considered either separately or combined as net biomass growth simulated using radiation use efficiency (RUE)8; and (4) the senescence, sterility or abortion of plant organs. All 29 models simulate these processes, except for sterility and abortion, in response to temperature change. Here, we compare the temperature functions of these four categories of physiological processes built into the 29 wheat models and identify the representative response types. We analyse how different temperature response functions affected simulations of wheat growth compared to observations in a field experiment8,9,10, in which well-fertilized and irrigated wheat grew under contrasting sowing dates and temperature environments (Hot Serial Cereal (HSC) experiment). We further evaluate the impact of the different response types by implementing them in two models (APSIM and SiriusQuality) and analysing their results against the HSC data and an additional global dataset from the International Heat Stress Genotpye Experiment (IHSGE)8 carried out by the International Maize and Wheat Improvement Center (CIMMYT). More importantly, we derive, based on newest knowledge and data, a set of new temperature response functions for the key physiological processes of wheat and demonstrate that when substituted in four wheat models the new functions reduced the error in grain yield simulations across seven global sites with different temperature regimes covered by the IHSGE data.