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: The need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems.  The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics.  In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models.

Abstract: Different datasets have been completed and are now available for the analysis of interannual  and seasonal variations of productive, reproductive or health data relative to  intensively dairy cows and also to establish the relationships between temperature  humidity index (THI) and dairy cow performances. Datasets are referred to different  European countries (Italy, Belgium, Luxembourg and Slovenia) with different climatic  features. All these datasets have data relative to Animal Pedigree (Cow ID, Birth date,  Breed, Sire ID and Dam ID), Test-day records (Cow ID, Herd ID, Parity, Calving date, Test  date, Milk yield, Milk fat and protein (%), Milk somatic cell score), Reproductive events  (Cow ID, Herd ID, Parity, Calving date, AI date, Sire ID, Days Open, NRR-56 day), and Daily  meteorological records (Meteo station ID, Zip code of the meteo station, Observation date,  Max temperature, Min temperature, Mean temperature, Max relative humidity, Min  relative humidity, Mean relative humidity, Solar radiation, Wind speed). The dataset  relative to Italy includes also Mortality data (Animal ID, Herd ID, Death date) and Bulk milk  quality data (Herd ID, Test date, Fat & protein (%), Somatic cell score, Bacterial count,  Herd latitude, Herd longitude, Herd elevation). An additional database is still under  construction and will be based on Spanish data from organic dairy farms. No Label