• Energy Research

Abstract Wheat grain yield is anticipated to suffer from the increased temperatures expected under climate change. In particular, the effects of post-anthesis temperatures on grain growth and development must be better understood in order to improve crop models. Grain growth and development involve several processes, and we hypothesized that some of the most important processes, namely grain dry biomass and water accumulation, grain volume expansion, and endosperm cell proliferation, will have different thermal sensitivity. To assess this, we established temperature–response curves of these processes for steady post-anthesis temperatures between 15 °C and 36 °C. From anthesis to maturity, grain dry mass, water mass, volume, and endosperm cell number were monitored, whilst considering grain temperature. Different sensitivities to heat of these various processes were revealed. The rate of grain dry biomass accumulation increased linearly up to 25 °C, while the reciprocal of its duration increased linearly up to at least 32 °C. In contrast, the growth rates of traits contributing to grain expansion, such as increase in grain volume and cell numbers, had higher optimum temperatures, while the reciprocal of their durations were significantly lower. These temperature–response curves can contribute to improve current crop models, and allow targeting of specific mechanisms for genetic and genomic studies.

Abstract Grain filling is a critical process for improving crop production under adverse conditions caused by climate change. Here, using a quantitative method, we quantified post-anthesis source–sink relationships of a large dataset to assess the contribution of remobilized pre-anthesis assimilates to grain growth for both biomass and nitrogen. The dataset came from 13 years of semi-controlled field experimentation, in which six bread wheat genotypes were grown at plot scale under contrasting temperature, water, and nitrogen regimes. On average, grain biomass was ~10% higher than post-anthesis above-ground biomass accumulation across regimes and genotypes. Overall, the estimated relative contribution (%) of remobilized assimilates to grain biomass became increasingly significant with increasing stress intensity, ranging from virtually nil to 100%. This percentage was altered more by water and nitrogen regimes than by temperature, indicating the greater impact of water or nitrogen regimes relative to high temperatures under our experimental conditions. Relationships between grain nitrogen demand and post-anthesis nitrogen uptake were generally insensitive to environmental conditions, as there was always significant remobilization of nitrogen from vegetative organs, which helped to stabilize the amount of grain nitrogen. Moreover, variations in the relative contribution of remobilized assimilates with environmental variables were genotype dependent. Our analysis provides an overall picture of post-anthesis source–sink relationships and pre-anthesis assimilate contributions to grain filling across (non-)environmental factors, and highlights that designing wheat adaptation to climate change should account for complex multifactor interactions.

AbstractA 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.

AbstractWheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.