Abstract: Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phe-notyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.

Abstract: Agricultural nitrous oxide (N2O) pollution resulting from the use of synthetic fertilizers represents a significant contribution to anthropogenic greenhouse gas emissions, providing a rationale for reduced use of nitrogen (N) fertilizers. Nitrogen limitation results in extensive systems rebalancing that remodels metabolism and defence processes. To analyse the regulation underpinning these responses, barley (Horedeum vulgare) seedlings were grown for 7 d under N-deficient conditions until net photosynthesis was 50% lower than in N-replete controls. Although shoot growth was decreased there was no evidence for the induction of oxidative stress despite lower total concentrations of N-containing antioxidants. Nitrogen-deficient barley leaves were rich in amino acids, sugars and tricarboxylic acid cycle intermediates. In contrast to N-replete leaves one-day-old nymphs of the green peach aphid (Myzus persicae) failed to reach adulthood when transferred to N-deficient barley leaves. Transcripts encoding cell, sugar and nutrient signalling, protein degradation and secondary metabolism were over-represented in N-deficient leaves while those associated with hormone metabolism were similar under both nutrient regimes with the exception of mRNAs encoding proteins involved in auxin metabolism and responses. Significant similarities were observed between the N-limited barley leaf transcriptome and that of aphid-infested Arabidopsis leaves. These findings not only highlight significant similarities between biotic and abiotic stress signalling cascades but also identify potential targets for increasing aphid resistance with implications for the development of sustainable agriculture.

Abstract: Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation.