Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Influence of an External Electric Field and Dissipative Tunneling on Recombination Radiation in Quantum Dots.

The effect of an external electric field and dissipative tunneling on the spectral intensity of recombination radiation in a quantum dot with an A+ + e impurity complex (a hole localized on a neutral acceptor interacting with an electron localized in the ground state of the quantum dot) is studied in the zero-radius potential model in the adiabatic approximation. The probability of dissipative tunneling of a hole is calculated in the one-instanton approximation. A high sensitivity of the recombination radiation intensity to the strength of the external electric field and to such parameters of the surrounding matrix (dissipative tunneling parameters) as temperature, the constant of interaction with the contact medium (or the heat-bath), and the frequency of phonon modes, has been revealed. It is shown that an external electric field leads to a shift of the recombination radiation threshold by several tens of meV, and a change in the parameters of dissipative tunneling has a noticeable effect on the spectral intensity of recombination radiation. It is shown that the resonant tunneling effect manifests itself in the form of "dips" in the field dependence of the spectral intensity of recombination radiation, which occur at certain values of the external electric field strength and temperature. This opens up certain prospects for the use of the considered system "quantum dot-impurity complex A+ + e" under conditions of dissipative tunneling for the study and diagnostics of biological objects.

Nebulized Non-Immunogenic Staphylokinase in the Mice Acute Lung Injury Model.

Acute lung injury (ALI) as a model of acute respiratory distress syndrome is characterized by inflammation, complex coagulation, and hematologic abnormalities which result in the formation of fibrin-platelet microthrombi in the pulmonary vessels with the rapid development of progressive respiratory dysfunction. We hypothesize that a nebulized fibrinolytic agent, non-immunogenic staphylokinase (nSta), may be useful for ALI therapy. First, the effect of the nebulized nSta (0.2 mg/kg, 1.0 mg/kg, or 2.0 mg/kg) on the coagulogram parameters was studied in healthy rats. ALI was induced in mice by nebulized administration of lipopolysaccharide (LPS) at a dose of 10 mg/kg. nSta (0.2 mg/kg, 0.4 mg/kg or 0.6 mg/kg) was nebulized 30 min, 24 h, and 48 h after LPS administration. The level of pro-inflammatory cytokines was determined in the blood on the 8th day after LPS and nSta administration. The assessment of lung damage was based on their weighing and microscopic analysis. Fibrin/fibrinogen deposition in the lungs was determined by immunohistochemistry. After nSta nebulization in healthy rats, the fibrinogen blood level as well as activated partial thromboplastin time and prothrombin time did not change. In the nebulized ALI model, the mice showed an increase in lung weight due to their edema and rising fibrin deposition. An imbalance of proinflammatory cytokines was also found. Forty percent of mice with ALI without nSta nebulization had died. Nebulized nSta at a dose of 0.2 mg/kg reduced the severity of ALI: a decrease in interstitial edema and inflammatory infiltration was noted. At a dose of 0.4 mg/kg of nebulized nSta, the animals showed no peribronchial edema and the bronchi had an open clear lumen. At a dose of 0.6 mg/kg of nebulized nSta, the manifestations of ALI were completely eliminated. A significant dose-dependent reduction of the fibrin-positive areas in the lungs of mice with ALI was established. Nebulized nSta had a normalizing effect on the proinflammatory cytokines in blood- interleukin (IL)-1α, IL-17A, IL-6, and granulocyte-macrophage colony-stimulating factor (GM-CSF). These data showed the effectiveness of nebulized nSta and the perspectives of its clinical usage in COVID-19 patients with acute respiratory distress syndrome (ARDS).

Temperature sensitivity of SOM decomposition is linked with a K-selected microbial community.

Temperature sensitivity (Q10 ) of soil organic matter (SOM) decomposition is a crucial parameter to predict the fate of soil carbon (C) under global warming. Nonetheless, the response pattern of Q10 to continuous warming and the underlying mechanisms are still under debate, especially considering the complex interactions between Q10 , SOM quality, and soil microorganisms. We examined the Q10 of SOM decomposition across a mean annual temperature (MAT) gradient from -1.9 to 5.1°C in temperate mixed forest ecosystems in parallel with SOM quality and bioavailability, microbial taxonomic composition, and functional genes responsible for organic carbon decomposition. Within this temperature gradient of 7.0°C, the Q10 values increased with MAT, but decreased with SOM bioavailability. The Q10 values increased with the prevalence of K-strategy of soil microbial community, which was characterized by: (i) high ratios of oligotrophic to copiotrophic taxa, (ii) ectomycorrhizal to saprotrophic fungi, (iii) functional genes responsible for degradation of recalcitrant to that of labile C, and (iv) low average 16S rRNA operon copy number. Because the recalcitrant organic matter was mainly utilized by the K-strategists, these findings independently support the carbon quality-temperature theory from the perspective of microbial taxonomic composition and functions. A year-long incubation experiment was performed to determine the response of labile and recalcitrant C pools to warming based on the two-pool model. The decomposition of recalcitrant SOM was more sensitive to increased temperature in southern warm regions, which might attribute to the dominance of K-selected microbial communities. It implies that climate warming would mobilize the larger recalcitrant pools in warm regions, exacerbating the positive feedback between increased MAT and CO2 efflux. This is the first attempt to link temperature sensitivity of SOM decomposition with microbial eco-strategies by incorporating the genetic information and disentangling the complex relationship between Q10 and soil microorganisms.

Rovibronic spectroscopy of PN from first principles.

We report an ab initio study on the rovibronic spectroscopy of the closed-shell diatomic molecule phosphorous mononitride, PN. The study considers the nine lowest electronic states, X 1Σ+, A 1Π, C 1Σ-, D 1Δ, E 1Σ-, a 3Σ+, b 3Π, d 3Δ and e 3Σ- using high level electronic structure theory and accurate nuclear motion calculations. The ab initio data cover 9 potential energy, 14 spin-orbit coupling, 7 electronic angular momentum coupling, 9 electric dipole moment and 8 transition dipole moment curves. The Duo nuclear motion program is used to solve the coupled nuclear motion Schrödinger equations for these nine electronic states and to simulate rovibronic absorption spectra of 31P14N for different temperatures, which are compared to available spectroscopic studies. Lifetimes for all states are calculated and compared to previous results from the literature. The calculated lifetime of the A1Π state shows good agreement with an experimental value from the literature, which is an important quality indicator for the ab initio A-X transition dipole moment.

Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time.

Projections based on invariant genotypes and agronomic practices indicate that climate change will largely decrease crop yields. The comparatively few studies considering farmers' adaptation result in a diversity of impacts depending on their assumptions. We combined experiments and process-based modeling for analyzing the consequences of climate change on European maize yields if farmers made the best use of the current genetic variability of cycle duration, based on practices they currently use. We first showed that the genetic variability of maize flowering time is sufficient for identifying a cycle duration that maximizes yield in a range of European climatic conditions. This was observed in six field experiments with a panel of 121 accessions and extended to 59 European sites over 36 years with a crop model. The assumption that farmers use optimal cycle duration and sowing date was supported by comparison with historical data. Simulations were then carried out for 2050 with 3 million combinations of crop cycle durations, climate scenarios, management practices, and modeling hypotheses. Simulated grain production over Europe in 2050 was stable (-1 to +1%) compared with the 1975-2010 baseline period under the hypotheses of unchanged cycle duration, whereas it was increased (+4-7%) when crop cycle duration and sowing dates were optimized in each local environment. The combined effects of climate change and farmer adaptation reduced the yield gradient between south and north of Europe and increased European maize production if farmers continued to make the best use of the genetic variability of crop cycle duration.

Proliferative Capacity of Adult Mouse Brain.

We studied cell proliferation in the postnatal mouse brain between the ages of 2 and 30 months and identified four compartments with different densities of proliferating cells. The first identified compartment corresponds to the postnatal pallial neurogenic (PPN) zone in the telencephalon; the second to the subpallial postnatal neurogenic (SPPN) zone in the telencephalon; the third to the white matter bundles in the telencephalon; and the fourth to all brain parts outside of the other three compartments. We estimated that about 3.4 million new cells, including 0.8 million in the subgranular zone (SGZ) in the hippocampus, are produced in the PPN zone. About 21 million new cells, including 10 million in the subependymal zone (SEZ) in the lateral walls of the lateral ventricle and 2.7 million in the rostral migratory stream (RMS), are produced in the SPPN zone. The third and fourth compartments together produced about 31 million new cells. The analysis of cell proliferation in neurogenic zones shows that postnatal neurogenesis is the direct continuation of developmental neurogenesis in the telencephalon and that adult neurogenesis has characteristics of the late developmental process. As a developmental process, adult neurogenesis supports only compensatory regeneration, which is very inefficient.

Does fresh farmyard manure introduce surviving microbes into soil or activate soil-borne microbiota?

Manure inputs into soil strongly affect soil microbial communities leading to shifts in microbial diversity and activity. It is still not clear whether these effects are caused mainly by the survival of microbes introduced with manure or by activation of the soil-borne microbiome. Here, we investigated how the soil microbiome was changed after the introduction of fresh farmyard cattle manure, and which microorganisms originating from manure survived in soil. Manure addition led to a strong increase in soil microbial biomass, gene copies abundances, respiration activity, and diversity. High-throughput sequencing analysis showed that higher microbial diversity in manured soil was caused mainly by activation of 113 soil-borne microbial genera which were mostly minor taxa in not-fertilized soil. Two weeks after manure input, 78% of the manure-associated genera were not detected anymore. Only 15 of 237 prokaryotic genera that originated from manure survived for 144 days in soil, and only 8 of them (primarily representatives of Clostridia class) were found in manured soil after winter. Thus, an increase in microbial biomass and diversity after manure input is caused mainly by activation of soil-borne microbial communities, while most exogenous microbes from manure do not survive in soil conditions after few months.

Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity.

The productivity of permanent temperate cut grasslands is mainly driven by weather, soil characteristics, botanical composition and management. To adapt management to climate change, adjusting the cutting dates to reflect earlier onset of growth and expansion of the vegetation period is particularly important. Simulations of cut grassland productivity under climate change scenarios demands management settings to be dynamically derived from actual plant development rather than using static values derived from current management operations. This is even more important in the alpine region, where the predicted temperature increase is twice as high as compared to the global or Northern Hemispheric average. For this purpose, we developed a dynamic management module that provides timing of cutting and manuring events when running the biogeochemical model LandscapeDNDC. We derived the dynamic management rules from long-term harvest measurements and monitoring data collected at pre-alpine grassland sites located in S-Germany and belonging to the TERENO monitoring network. We applied the management module for simulations of two grassland sites covering the period 2011-2100 and driven by scenarios that reflect the two representative concentration pathways (RCP) 4.5 and 8.5 and evaluated yield developments of different management regimes. The management module was able to represent timing of current management operations in high agreement with several years of field observations (r² > 0.88). Even more, the shift of the first cutting dates scaled to a +1 °C temperature increase simulated with the climate change scenarios (-9.1 to -17.1 days) compared well to the shift recorded by the German Weather Service (DWD) in the study area from 1991-2016 (-9.4 to -14.0 days). In total, the shift in cutting dates and expansion of the growing season resulted in 1-2 additional cuts per year until 2100. Thereby, climate change increased yields of up to 6 % and 15 % in the RCP 4.5 and 8.5 scenarios with highest increases mainly found for dynamically adapted grassland management going along with increasing fertilization rates. In contrast, no or only minor yield increases were associated with simulations restricted to fertilization rates of 170 kg N ha-1 yr-1 as required by national legislations. Our study also shows that yields significantly decreased in drought years, when soil moisture is limiting plant growth but due to comparable high precipitation and water holding capacity of soils, this was observed mainly in the RCP 8.5 scenario in the last decades of the century.

Adverse weather conditions for UK wheat production under climate change.

Winter wheat is an important crop in the UK, suited to the typical weather conditions in the current climate. In a changing climate the increased frequency and severity of adverse weather events, which are often localised, are considered a major threat to wheat production. In the present study we assessed a range of adverse weather conditions, which can significantly affect yield, under current and future climates based on adverse weather indices. We analysed changes in the frequency, magnitude and spatial patterns of 10 adverse weather indices, at 25 sites across the UK, using climate scenarios from the CMIP5 ensemble of global climate models (GCMs) and two greenhouse gas emissions (RCP4.5 and RCP8.5). The future UK climate is expected to remain favourable for wheat production, with most adverse weather indicators reducing in magnitude by the mid-21st century. Hotter and drier summers would improve sowing and harvesting conditions and reduce the risk of lodging. The probability of late frosts and heat stress during reproductive and grain filling periods would likely remain small in 2050. Wetter winter and spring could cause issues with waterlogging. The severity of drought stress during reproduction would generally be lower in 2050, however localised differences suggest it is important to examine drought at a small spatial scale. Prolonged water stress does not increase considerably in the UK, as may be expected in other parts of Europe. Climate projections based on the CMIP5 ensemble reveal considerable uncertainty in the magnitude of adverse weather conditions including waterlogging, drought and water stress. The variation in adverse weather conditions due to GCMs was generally greater than between emissions scenarios. Accordingly, CMIP5 ensembles should be used in the assessment of adverse weather conditions for crop production to indicate the full range of possible impacts, which a limited number of GCMs may not provide.

Investigating the effects of inter-annual weather variation (1968-2016) on the functional response of cereal grain yield to applied nitrogen, using data from the Rothamsted Long-Term Experiments.

The effect of weather on inter-annual variation in the crop yield response to nitrogen (N) fertilizer for winter wheat (Triticum aestivvum L.) and spring barley (Hordeum vulgare L.) was investigated using yield data from the Broadbalk Wheat and Hoosfield Spring Barley long-term experiments at Rothamsted Research. Grain yields of crops from 1968 to 2016 were modelled as a function of N rates using a linear-plus-exponential (LEXP) function. The extent to which inter-annual variation in the parameters of these responses was explained by variations in weather (monthly summarized temperatures and rainfall), and by changes in the cultivar grown, was assessed. The inter-annual variability in rainfall and underlying temperature influenced the crop N response and hence grain yields in both crops. Asymptotic yields in wheat were particularly sensitive to mean temperature in November, April and May, and to total rainfall in October, February and June. In spring barley asymptotic yields were sensitive to mean temperature in February and June, and to total rainfall in April to July inclusive and September. The method presented here explores the separation of agronomic and environmental (weather) influences on crop yield over time. Fitting N response curves across multiple treatments can support an informative analysis of the influence of weather variation on the yield variability. Whilst there are issues of the confounding and collinearity of explanatory variables within such models, and that other factors also influence yields over time, our study confirms the considerable impact of weather variables at certain times of the year. This emphasizes the importance of including weather temporal variation when evaluating the impacts of climate change on crops.

Drought tolerance during reproductive development is important for increasing wheat yield potential under climate change in Europe.

Drought stress during reproductive development could drastically reduce wheat grain number and yield, but quantitative evaluation of such an effect is unknown under climate change. The objectives of this study were to evaluate potential yield benefits of drought tolerance during reproductive development for wheat ideotypes under climate change in Europe, and to identify potential cultivar parameters for improvement. We used the Sirius wheat model to optimize drought-tolerant (DT) and drought-sensitive (DS) wheat ideotypes under a future 2050 climate scenario at 13 contrasting sites, representing major wheat growing regions in Europe. Averaged over the sites, DT ideotypes achieved 13.4% greater yield compared with DS, with higher yield stability. However, the performances of the ideotypes were site dependent. Mean yield of DT was 28-37% greater compared with DS in southern Europe. In contrast, no yield difference (≤1%) between ideotypes was found in north-western Europe. An intermediate yield benefit of 10-23% was found due to drought tolerance in central and eastern Europe. We conclude that tolerance to drought stress during reproductive development is important for high yield potentials and greater yield stability of wheat under climate change in Europe.

Global wheat production with 1.5 and 2.0°C above pre-industrial warming.

Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Raising genetic yield potential in high productive countries: Designing wheat ideotypes under climate change.

Designing crop ideotype is an important step to raise genetic yield potential in a target environment. In the present study, we designed wheat ideotypes based on the state-of-the-art knowledge in crop physiology to increase genetic yield potential for the 2050-climate, as projected by the HadGEM2 global climate model for the RCP8.5 emission scenario, in two high-wheat-productive countries, viz. the United Kingdom (UK) and New Zealand (NZ). Wheat ideotypes were optimized to maximize yield potential for both water-limited (IW2050 ) and potential (IP2050 ) conditions by using Sirius model and exploring the full range of cultivar parameters. On average, a 43-51% greater yield potential over the present winter wheat cv. Claire was achieved for IW2050 in the UK and NZ, whereas a 51-62% increase was obtained for IP2050 . Yield benefits due to the potential condition over water-limitation were small in the UK, but 13% in NZ. The yield potentials of wheat were 16% (2.6 t ha-1) and 31% (5 t ha-1) greater in NZ than in the UK under 2050-climate in water-limited and potential conditions respectively. Modelling predicts the possibility of substantial increase in genetic yield potential of winter wheat under climate change in high productive countries. Wheat ideotypes optimized for future climate could provide plant scientists and breeders with a road map for selection of the target traits and their optimal combinations for wheat improvement and genetic adaptation to raise the yield potential.

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments.

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.

Multimodel ensembles improve predictions of crop-environment-management interactions.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Amyloid ß synaptotoxicity is Wnt-PCP dependent and blocked by fasudil.

INTRODUCTION: Synapse loss is the structural correlate of the cognitive decline indicative of dementia. In the brains of Alzheimer's disease sufferers, amyloid ß (Aß) peptides aggregate to form senile plaques but as soluble peptides are toxic to synapses. We previously demonstrated that Aß induces Dickkopf-1 (Dkk1), which in turn activates the Wnt-planar cell polarity (Wnt-PCP) pathway to drive tau pathology and neuronal death. METHODS: We compared the effects of Aß and of Dkk1 on synapse morphology and memory impairment while inhibiting or silencing key elements of the Wnt-PCP pathway. RESULTS: We demonstrate that Aß synaptotoxicity is also Dkk1 and Wnt-PCP dependent, mediated by the arm of Wnt-PCP regulating actin cytoskeletal dynamics via Daam1, RhoA and ROCK, and can be blocked by the drug fasudil. DISCUSSION: Our data add to the importance of aberrant Wnt signaling in Alzheimer's disease neuropathology and indicate that fasudil could be repurposed as a treatment for the disease.

Heat tolerance around flowering in wheat identified as a key trait for increased yield potential in Europe under climate change.

To deliver food security for the 9 billon population in 2050, a 70% increase in world food supply will be required. Projected climatic and environmental changes emphasize the need for breeding strategies that delivers both a substantial increase in yield potential and resilience to extreme weather events such as heat waves, late frost, and drought. Heat stress around sensitive stages of wheat development has been identified as a possible threat to wheat production in Europe. However, no estimates have been made to assess yield losses due to increased frequency and magnitude of heat stress under climate change. Using existing experimental data, the Sirius wheat model was refined by incorporating the effects of extreme temperature during flowering and grain filling on accelerated leaf senescence, grain number, and grain weight. This allowed us, for the first time, to quantify yield losses resulting from heat stress under climate change. The model was used to optimize wheat ideotypes for CMIP5-based climate scenarios for 2050 at six sites in Europe with diverse climates. The yield potential for heat-tolerant ideotypes can be substantially increased in the future (e.g. by 80% at Seville, 100% at Debrecen) compared with the current cultivars by selecting an optimal combination of wheat traits, e.g. optimal phenology and extended duration of grain filling. However, at two sites, Seville and Debrecen, the grain yields of heat-sensitive ideotypes were substantially lower (by 54% and 16%) and more variable compared with heat-tolerant ideotypes, because the extended grain filling required for the increased yield potential was in conflict with episodes of high temperature during flowering and grain filling. Despite much earlier flowering at these sites, the risk of heat stress affecting yields of heat-sensitive ideotypes remained high. Therefore, heat tolerance in wheat is likely to become a key trait for increased yield potential and yield stability in southern Europe in the future.

In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management.

Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.