Abstract: Polyphenol oxidase (PPO) catalyses the oxidation of monophenols and/or o-diphenols to o-quinones with the concomitant reduction of oxygen to water which results in protein complexing and the formation of brown melanin pigments. The most frequently suggested role for PPO in plants has been in defence against herbivores and pathogens, based on the physical separation of the chloroplast-localized enzyme from the vacuole-localized substrates. The o-quinone-protein complexes, formed as a consequence of cell damage, may reduce the nutritional value of the tissue and thereby reduce predation but can also participate in the formation of structural barriers against invading pathogens. However, since a sufficient level of compartmentation-based regulation could be accomplished if PPO was targeted to the cytosol, the benefit derived by some plant species in having PPO present in the chloroplast lumen remains an intriguing question. So is there more to the chloroplastic location of PPO? An interaction between PPO activity and photosynthesis has been proposed on more than one occasion but, to date, evidence either for or against direct involvement has been equivocal, and the lack of identified chloroplastic substrates remains an issue. Similarly, PPO has been suggested to have both pro- and anti-oxidant functions. Nevertheless, several independent lines of evidence suggest that PPO responds to environmental conditions and could be involved in the response of plants to abiotic stress. This review highlights our current understanding of the in vivo functions of PPO and considers the potential opportunities it presents for exploitation to increase stress tolerance in food crops.

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.