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 di erent wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 • C to 32 • 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 • C of further temperature increase and become more variable over space and time.Understanding how different climate factors interact and impact food production 3 is essential when reaching decisions on how to adapt to the effects of climate change. To implement such strategies the contribution of various climate variables on crop yields need to be separated and quantified. For instance, a change in temperature will require a different adaptation strategy than a change in rainfall 4 . Temperature changes alone are reported to have potentially large negative impacts on crop production 5 , and hotspots-locations where plants suffer from high temperature stress-have been identified across the globe 6,7 . Crop simulation models are useful tools in climate impact studies as they deal with multiple climate factors and how they interact with various crop growth and yield formation processes that are sensitive to climate. These models have been applied in many studies, including the assessment of temperature impacts on crop production 1,8 . However, none of the crop models have been tested systematically against experiments at different temperatures in field conditions. Although many glasshouse and controlled-environment temperature experiments have been described, they are often not suitable for model testing as the heating of root systems in pots 9 and effects on micro-climate differ greatly from field conditions 10 . Detailed information on field experiments with a wide range of sowing dates and infrared heating recently became available for wheat 11,12 . Such experiments are well suited for testing the ability of crop models to quantify temperature responses under field conditions. Testing the temperature responses of crop models is particularly important for assessing the impact of climate change on wheat production, because the largest uncertainty in simulated impacts on yield arises from increasing temperatures 2 .In a 'Hot Serial Cereal' (HSC) well-irrigated and fertilized experiment with a single cultivar, the observed days after sowing (DAS) to maturity declined...

Although silicon (Si) is the second most abundant element both on the surface of the Earth's crust and in soils, it has not yet been listed among the essential elements for higher plants. However, the beneficial role of Si in stimulating the growth and development of many plant species has been generally recognized. Silicon is known to effectively mitigate various abiotic stresses such as manganese, aluminum and heavy metal toxicities, and salinity, drought, chilling and freezing stresses. However, mechanisms of Si-mediated alleviation of abiotic stresses remain poorly understood. The key mechanisms of Si-mediated alleviation of abiotic stresses in higher plants include: (1) stimulation of antioxidant systems in plants, (2) complexation or co-precipitation of toxic metal ions with Si, (3) immobilization of toxic metal ions in growth media, (4) uptake processes, and (5) compartmentation of metal ions within plants. Future research needs for Si-mediated alleviation of abiotic stresses are also discussed.

A microarray accession number was inadvertently omitted from this paper. Microarray data for this paper can be found at NCBI GEO (http://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE16062.

Around all human activity, there are zones of pollution with pesticides, heavy metals, pharmaceuticals, personal care products and the microorganisms associated with human waste streams and agriculture. This diversity of pollutants, whose concentration varies spatially and temporally, is a major challenge for monitoring. Here, we suggest that the relative abundance of the clinical class 1 integron-integrase gene, intI1, is a good proxy for pollution because: (1) intI1 is linked to genes conferring resistance to antibiotics, disinfectants and heavy metals; (2) it is found in a wide variety of pathogenic and nonpathogenic bacteria; (3) its abundance can change rapidly because its host cells can have rapid generation times and it can move between bacteria by horizontal gene transfer; and (4) a single DNA sequence variant of intI1 is now found on a wide diversity of xenogenetic elements, these being complex mosaic DNA elements fixed through the agency of human selection. Here we review the literature examining the relationship between anthropogenic impacts and the abundance of intI1, and outline an approach by which intI1 could serve as a proxy for anthropogenic pollution.

An extensive data set of total arsenic analysis for 901 polished (white) grain samples, originating from 10 countries from 4 continents, was compiled. The samples represented the baseline (i.e., not specifically collected from arsenic contaminated areas), and all were for market sale in major conurbations. Median total arsenic contents of rice varied 7-fold, with Egypt (0.04 mg/kg) and India (0.07 mg/kg) having the lowest arsenic content while the U.S. (0.25 mg/kg) and France (0.28 mg/kg) had the highest content. Global distribution of total arsenic in rice was modeled by weighting each country's arsenic distribution by that country's contribution to global production. A subset of 63 samples from Bangladesh, China, India, Italy, and the U.S. was analyzed for arsenic species. The relationship between inorganic arsenic content versus total arsenic content significantly differed among countries, with Bangladesh and India having the steepest slope in linear regression, and the U.S. having the shallowest slope. Using country-specific rice consumption data, daily intake of inorganic arsenic was estimated and the associated internal cancer risk was calculated using the U.S.Environmental Protection Agency (EPA) cancer slope. Median excess internal cancer risks posed by inorganic arsenic ranged 30-fold for the 5 countries examined, being 0.7 per 10,000 for Italians to 22 per 10,000 for Bangladeshis, when a 60 kg person was considered.

Summary The abundance and composition of soil ammonia‐oxidizing bacteria (AOB) and ammonia‐oxidizing archaea (AOA) were investigated by using quantitative real‐time polymerase chain reaction, cloning and sequencing approaches based on amoA genes. The soil, classified as agri‐udic ferrosols with pH (H2O) ranging from 3.7 to 6.0, was sampled in summer and winter from long‐term field experimental plots which had received 16 years continuous fertilization treatments, including fallow (CK0), control without fertilizers (CK) and those with combinations of fertilizer nitrogen (N), phosphorus (P) and potassium (K): N, NP, NK, PK, NPK and NPK plus organic manure (OM). Population sizes of AOB and AOA changed greatly in response to the different fertilization treatments. The NPK + OM treatment had the highest copy numbers of AOB and AOA amoA genes among the treatments that received mineral fertilizers, whereas the lowest copy numbers were recorded in the N treatment. Ammonia‐oxidizing archaea were more abundant than AOB in all the corresponding treatments, with AOA to AOB ratios ranging from 1.02 to 12.36. Significant positive correlations were observed among the population sizes of AOB and AOA, soil pH and potential nitrification rates, indicating that both AOB and AOA played an important role in ammonia oxidation in the soil. Phylogenetic analyses of the amoA gene fragments showed that all AOB sequences from different treatments were affiliated with Nitrosospira or Nitrosospira‐like species and grouped into cluster 3, and little difference in AOB community composition was recorded among different treatments. All AOA sequences fell within cluster S (soil origin) and cluster M (marine and sediment origin). Cluster M dominated exclusively in the N, NP, NK and PK treatments, indicating a pronounced difference in the community composition of AOA in response to the long‐term fertilization treatments. These findings could be fundamental to improve our understanding of the importance of both AOB and AOA in the cycling of nitrogen and other nutrients in terrestrial ecosystems.

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China faces great challenges in protecting its soil from contamination caused by rapid industrialization and urbanization over the last three decades. Recent nationwide surveys show that 16% of the soil samples, 19% for the agricultural soils, are contaminated based on China’s soil environmental quality limits, mainly with heavy metals and metalloids. Comparisons with other regions of the world show that the current status of soil contamination, based on the total contaminant concentrations, is not worse in China. However, the concentrations of some heavy metals in Chinese soils appear to be increasing at much greater rates. Exceedance of the contaminant limits in food crops is widespread in some areas, especially southern China, due to elevated inputs of contaminants, acidic nature of the soil and crop species or cultivars prone to heavy metal accumulation. Minimizing the transfer of contaminants from soil to the food chain is a top priority. A number of options are proposed, including identification of the sources of contaminants to agricultural systems, minimization of contaminant inputs, reduction of heavy metal phytoavailability in soil with liming or other immobilizing materials, selection and breeding of low accumulating crop cultivars, adoption of appropriate water and fertilizer management, bioremediation, and change of land use to grow nonfood crops. Implementation of these strategies requires not only technological advances, but also social-economic evaluation and effective enforcement of environmental protection law.