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Global warming is characterized by shifts in weather patterns and increases in climatic variability and extreme events. New wheat cultivars will be required for a rapidly changing environment, putting severe pressure on breeders who must select for climate conditions which can only be predicted with a great degree of uncertainty. To assist breeders to identify key wheat traits for improvements under climate change, wheat ideotypes can be designed and tested in silico using a wheat simulation model for a wide range of future climate scenarios predicted by global climate models. A wheat ideotype is represented by a set of cultivar parameters in a model, which could be optimized for best wheat performance under projected climate change. As an example, high-yielding wheat ideotypes were designed at two contrasting European sites for the 2050 (A1B) climate scenario. Simulations showed that wheat yield potential can be substantially increased for new ideotypes compared with current wheat varieties under climate change. The main factors contributing to yield increase were improvement in light conversion efficiency, extended duration of grain filling resulting in a higher harvest index, and optimal phenology. [ABSTRACT FROM AUTHOR]

Abstract: Availability of water for plant growth is a key factor determining plant distribution in natural ecosystems and is the most important limiting factor in agricultural systems. The high environmental and economical cost of irrigation, required to maintain grain yields in water scarce environments, gives an incentive for improvements in water use efficiency of the crop. The objective of our study is to quantify the effects of changes in simple component plant traits on wheat yield under limited water supplies using a modelling approach. The Sirius wheat simulation model was used to perform analyses at two contrasting European sites, Rothamsted, UK and Seville, Spain, which represent major wheat growing areas in these countries. Several physiological traits were analysed to explore their effects on yield, including drought avoidance traits such as those controlling wheat development (phyllochron and grain filling duration), canopy expansion (maximum surface area of culm leaves) and water uptake (root vertical expansion rate and efficiency of water extraction) and drought tolerance traits such as responses of biomass accumulation and leaf senescence to water stress. Changes in parameters that control the effect of water stress on leaf senescence and biomass accumulation had the largest impact on grain yield under drought. The modified cultivar produced up to 70% more yield compared with the control for very dry years. Changes in phenology parameters, phyllochron and grain filling duration, did not improve yields at either site, suggesting that these parameters have been already optimised for climates in the UK and Spain through the breeding process. Our analysis illustrates the power of modelling in exploring and understanding complex traits in wheat. This may facilitate genetic research by focusing on experimental studies of component traits with the highest potential to influence crop performance. [Copyright &y& Elsevier]

Abstract: Analysis of possible impacts of climate change on agriculture, based on process-based simulation models, which use daily weather as their input, requires climate change scenarios with high spatial and temporal resolutions. Despite improvements in the performance of global and regional climate models, direct daily outputs from them are not suitable for such analysis. A methodology for construction of daily site-specific climate scenarios, based on a stochastic weather generator, is described. Initially the LARS-WG stochastic weather generator, used in our study, was calibrated for current climate with observed daily data. Then its parameters were adjusted for climate change, using the output from UKCIP02 projections, presented as changes in monthly mean climatic variables between the control run and future scenarios. To be able to generate scenarios at any given location in the UK, parameters of LARS-WG, computed for locations with long historical weather records, were interpolated over the UK. Distributions for climatic variables were interpolated locally and then modified by globally interpolated mean values to account for the effect of topography. As illustrations, daily UKCIP02-based scenarios were generated and used to calculate various weather extreme events and impact of climate change on wheat growth. Under a warmer climate, extreme statistics related to temperature, such as heat-waves, are likely to increase substantially in magnitude and frequency. Two impact statistics for wheat, i.e. drought stress index and probability of an episode of hot temperature after anthesis, were analysed. Despite higher temperature and lower summer precipitation for the 2080HI scenario, the relative impact on yield due to drought stress is smaller for 2080HI than for the baseline climate, because of the ability of wheat to mature early in a warmer climate avoiding summer heat and drought stress. [Copyright &y& Elsevier]

Abstract: Cereal producers are under pressure to increase yields and maintain profitability against a background of environmental constraints and high fertiliser costs. The production of high yields requires high inputs of N, and excessive N can lead to pollution of watercourses. This provides an incentive for the maximisation of nitrogen use efficiency (NUE), defined as grain yield per unit available soil N from all sources. Routes to the improvement of NUE may be through selection of an appropriate environment for the crop, better management or crop genetic improvement. However, the relative importance of these choices is poorly understood. Here we have used a modelling approach to quantify the effects of these factors on NUE. We performed an analysis using the Sirius wheat simulation model for a range of N treatments at two contrasting European sites: Rothamsted, UK and Seville, Spain. Several simple crop traits were selected for sensitivity analysis of NUE. These included traits controlling wheat development, determining sizes of N storage pools in the plant and traits responsible for uptake-efficiency of roots for water and N. We used Sirius because it is based on simple, mechanistic descriptions of wheat phenology and nitrogen uptake and redistribution, which makes it possible to link model cultivar parameters with simple physiological traits. Our analysis showed that weather and N management are the source of large variations in NUE. At Rothamsted, where water was not a limiting factor, N treatments produce more variation in NUE (∼51%) than weather (∼32%). At Seville, where water is limited, weather was responsible for larger variation in NUE (for a shallow soil and low N treatment up to ∼100%) compared with ∼40% for N treatments. Two traits (leaf [N] and phyllochron) out of six showed potential for improvement of NUE. A decrease in leaf [N] increased NUE by 10–15%, when N was limiting, but for high N supply the effect on NUE was negligible. Increasing phyllochron to delay flowering produced up to 15% increase in NUE at Rothamsted, but no increase at Seville. Our analysis demonstrated that a crop simulation model is a powerful tool for deconvoluting complex traits in wheat. This may facilitate genetic and subsequent genomics research by focusing experiments only on those wheat traits that are identified by the modelling study as the most promising. [Copyright &y& Elsevier]

Abstract: In order to use crop simulation models to predict crop yield, unobserved daily weather, an important input for crop models, must be forecast in some sense. Due to the chaotic nature of weather and the non-linear response of crop simulation models to weather input, this forecast weather cannot simply be a single weather series (e.g. average historical weather for the upcoming growing season), but must be an ensemble of weather series, incorporating site-specific climatic variability. To capture weather uncertainty, we used the LARS-WG stochastic weather generator to produce a probabilistic ensemble of weather series by mixing observed weather from the beginning of a season with stochastically generated (synthetic) weather for the remainder of the growing season. This ensemble was used with the crop simulation model Sirius to generate distributions of crop characteristics. Progressing through the growing season, as the proportion of synthetic weather in these ensembles decreased, the distribution means converged towards the true values, allowing us to make predictions with a high level of confidence before crop maturity. In this fashion, we analysed six sites with diverse climates in Europe and New Zealand, comparing lead-times for predicting different crop characteristics at various geographic locations. We demonstrated that that there is a large difference between lead-times amongst different crop characteristics at a single location, and that there is a large variation in lead-times for predicting selected crop characteristics between locations. Variation in climates places a quantifiable limit on our ability to make crop predictions using crop simulation models. [Copyright &y& Elsevier]

• Adverse weather conditions were analysed using a range of adverse weather indices. • Future UK climate is expected to remain favourable for wheat production. • Wetter winter and spring could, however, increase the risk of waterlogging. • Use of global climate model ensembles to quantify prediction uncertainty is advised. 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. [ABSTRACT FROM AUTHOR]