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: Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

Abstract: Process-based grassland models (PBMs) simulate growth and development of vegetation over time. The models tend to have a large number of parameters that represent properties of the plants. To simulate different cultivars of the same species, different parameter values are required. Parameter differences may be interpreted as genetic variation for plant traits. Despite this natural connection between PBMs and plant genetics, there are only few examples of successful use of PBMs in plant breeding. Here we present a new procedure by which PBMs can help design ideotypes, i.e. virtual cultivars that optimally combine properties of existing cultivars. Ideotypes constitute selection targets for breeding. The procedure consists of four steps: (1) Bayesian calibration of model parameters using data from cultivar trials, (2) Estimating genetic variation for parameters from the combination of cultivar-specific calibrated parameter distributions, (3) Identifying parameter combinations that meet breeding objectives, (4) Translating model results to practice, i.e. interpreting parameters in terms of practical selection criteria. We show an application of the procedure to timothy (Phleum pratense L.) as grown in different regions of Norway.