Data requirements for crop modelling-Applying the learning curve approach to the simulation of winter wheat flowering time under climate change

A prerequisite for application of crop models is a careful parameterization based on observational data. However, there are limited studies investigating the link between quality and quantity of observed data and its suitability for model parameterization. Here, we explore the interactions between number of measurements, noise and model predictive skills to simulate the impact of 2050′s climate change (RCP8.5) on winter wheat flowering time. The learning curve of two winter wheat phenology models is analysed under different assumptions about the size of the calibration dataset, the measurement error and the accuracy of the model structure. Our assessment confirms that prediction skills improve asymptotically with the size of the calibration dataset, as with statistical models. Results suggest that less precise but larger training datasets can improve the predictive abilities of models. However, the non-linear relationship between number of measurements, measurement error, and prediction skills limit the compensation between data quality and quantity. We find that the model performance does not improve significantly with a theoretical minimum size of 7–9 observations when the model structure is approximate. While simulation of crop phenology is critical to crop model simulation, more studies are needed to explore data needs for assessing entire crop models

Assessing uncertainty and complexity in regional-scale crop model simulations

Crop models are imperfect approximations to real world interactions between biotic and abiotic factors. In some situations, the uncertainties associated with choices in model structure, model inputs and parameters can exceed the spatiotemporal variability of simulated yields, thus limiting predictability. For Indian groundnut, we used the General Large Area Model for annual crops (GLAM) with an existing framework to decompose uncertainty, to first understand how skill changes with added model complexity, and then to determine the relevant uncertainty sources in yield and other prognostic variables (total biomass, leaf area index and harvest index). We developed an ensemble of simulations by perturbing GLAM parameters using two different input meteorology datasets, and two model versions that differ in the complexity with which they account for assimilation. We found that added complexity improved model skill, as measured by changes in the root mean squared error (RMSE), by 5-10% in specific pockets of western, central and southern India, but that 85% of the groundnut growing area either did not show improved skill or showed decreased skill from such added complexity. Thus, adding complexity or using overly complex models at regional or global scales should be exercised with caution. Uncertainty analysis indicated that, in situations where soil and air moisture dynamics are the major determinants of productivity, predictability in yield is high. Where uncertainty for yield is high, the choice of weather input data was found critical for reducing uncertainty. However, for other prognostic variables (including leaf area index, total biomass and the harvest index) parametric uncertainty was generally the most important source, with a contribution of up to 90% in some cases, suggesting that regional-scale data additional to yield to constrain model parameters is needed. Our study provides further evidence that regional-scale studies should explicitly quantify multiple uncertainty sources

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

C4 model grass Setaria is a short day plant with secondary long day genetic regulation

The effect of photoperiod (day:night ratio) on flowering time was investigated in the wild species, Setaria viridis, and in a set of recombinant inbred lines (RILs) derived from a cross between foxtail millet (S. italica) and its wild ancestor green foxtail (S. viridis). Photoperiods totaled 24 h, with three trials of 8:16, 12:12 and 16:8 light:dark hour regimes for the RIL population, and these plus 10:14 and 14:10 for the experiments with S. viridis alone. The response of S. viridis to light intensity as well as photoperiod was assessed by duplicating photoperiods at two light intensities (300 and 600 μmol.m-2.s-1). In general, day lengths longer than 12 h delayed flowering time, although flowering time was also delayed in shorter day-lengths relative to the 12 h trial, even when daily flux in high intensity conditions exceeded that of the low intensity 12 h trial. Cluster analysis showed that the effect of photoperiod on flowering time differed between sets of RILs, with some being almost photoperiod insensitive and others being delayed with respect to the population as a whole in either short (8 or 12 h light) or long (16 h light) photoperiods. QTL results reveal a similar picture, with several major QTL colocalizing between the 8 and 12 h light trials, but with a partially different set of QTL identified in the 16 h trial. Major candidate genes for these QTL include several members of the PEBP protein family that includes Flowering Locus T (FT) homologs such as OsHd3a, OsRFT1, and ZCN8/12. Thus, Setaria is a short day plant (flowering quickest in short day conditions) whose flowering is delayed by long day lengths in a manner consistent with the responses of most other members of the grass family. However, the QTL results suggest that flowering time under long day conditions uses additional genetic pathways to those used under short day conditions.Peer reviewedPlant Biology, Ecology and Evolutio

An explanatory model of temperature influence on flowering through whole-plant accumulation of <i>FT<b> </b></i>in <i>Arabidopsis thaliana</i><i> </i>

We assessed mechanistic temperature influence on flowering by incorporating temperature-responsive flowering mechanisms across developmental age into an existing model. Temperature influences the leaf production rate as well as expression of FLOWERING LOCUS T (FT), a photoperiodic flowering regulator that is expressed in leaves. The Arabidopsis Framework Model incorporated temperature influence on leaf growth but ignored the consequences of leaf growth on and direct temperature influence of FT expression. We measured FT production in differently aged leaves and modified the model, adding mechanistic temperature influence on FT transcription, and causing whole-plant FT to accumulate with leaf growth. Our simulations suggest that in long days, the developmental stage (leaf number) at which the reproductive transition occurs is influenced by day length and temperature through FT, while temperature influences the rate of leaf production and the time (in days) the transition occurs. Further, we demonstrate that FT is mainly produced in the first 10 leaves in the Columbia (Col-0) accession, and that FT accumulation alone cannot explain flowering in conditions in which flowering is delayed. Our simulations supported our hypotheses that: (i) temperature regulation of FT, accumulated with leaf growth, is a component of thermal time, and (ii) incorporating mechanistic temperature regulation of FT can improve model predictions when temperatures change over time

Climate change impact and adaptation for wheat protein

Wheat 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