Abstract: AdaptaOlive is a simplified physically-based model that has been developed to assess the behavior of olive under future climate conditions in Andalusia, southern Spain. The integration of different approaches based on experimental data from previous studies, combined with weather data from 11 climate models, is aimed at overcoming the high degree of uncertainty in the simulation of the response of agricultural systems under predicted climate conditions. The AdaptaOlive model was applied in a representative olive orchard in the Baeza area, one of the main producer zone in Spain, with the cultivar ‘Picual’. Simulations for the end of the 21st century showed olive oil yield increases of 7.1 and 28.9% under rainfed and full irrigated conditions, respectively, while irrigation requirements decreased between 0.5 and 6.2% for full irrigation and regulated deficit irrigation, respectively. These effects were caused by the positive impact of the increase in atmospheric CO2 that counterbalanced the negative impacts of the reduction in rainfall. The high degree of uncertainty associated with climate projections translated into a high range of yield and irrigation requirement projections, confirming the need for an ensemble of climate models in climate change impact assessment. The AdaptaOlive model also was applied for evaluating adaptation strategies related to cultivars, irrigation strategies and locations. The best performance was registered for cultivars with early flowering dates and regulated deficit irrigation. Thus, in the Baeza area full irrigation requirements were reduced by 12% and the yield in rainfed conditions increased by 7% compared with late flowering cultivars. Similarly, regulated deficit irrigation requirements and yield were reduced by 46% and 18%, respectively, compared with full irrigation. The results confirm the promise offered by these strategies as adaptation measures for managing an olive crop under semi-arid conditions in a changing climate.

Abstract: Climate change and its associated higher frequency and severity of adverse weather events require genotypic adaptation. Process-based ecophysiological modelling offers a powerful means to better target and accelerate development of new crop cultivars. Barley (Hordeum vulgare L) is an important crop throughout the world, and a good model for study of the genetics of stress adaptation because many quantitative trait loci and candidate genes for biotic and abiotic stress tolerance have been identified in it. Here, we developed a new approach to design future crop ideotypes using an ensemble of eight barley simulation models (i.e. APSIM, CropSyst, HERMES, MCWLA, MONICA, SIMPLACE, Sirius Quality, and WOFOST), and applied it to design climate-resilient barley ideotypes for Boreal and Mediterranean climatic zones in Europe. The results showed that specific barley genotypes, represented by sets of cultivar parameters in the crop models, could be promising under future climate change conditions, resulting in increased yields and low inter-annual yield variability. In contrast, other genotypes could result in substantial yield declines. The most favorable climate-zone-specific barley ideotypes were further proposed, having combinations of several key genetic traits in terms of phenology, leaf growth, photosynthesis, drought tolerance, and grain formation. For both Boreal and Mediterranean climatic zones, barley ideotypes under future climatic conditions should have a longer reproductive growing period, lower leaf senescence rate, larger radiation use efficiency or maximum assimilation rate, and higher drought tolerance. Such characteristics can produce substantial positive impacts on yields under contrasting conditions. Moreover, barley ideotypes should have a low photoperiod and high vernalization sensitivity for the Boreal climatic zone; for the Mediterranean, in contrast, it should have a low photoperiod and low vernalization sensitivity. The drought-tolerance trait is more beneficial for the Mediterranean than for the Boreal climatic zone. Our study demonstrates a sound approach to design future barley ideotypes based on an ensemble of well-tested, diverse crop models and on integration of knowledge from multiple disciplines. The robustness of model-aided ideotypes design can be further enhanced by continuously improving crop models and enhancing information exchange between modellers, agro-meteorologists, geneticists, physiologists, and plant breeders. (C) 2016 Elsevier B.V. All rights reserved.

Abstract: This study developed Adaptation Response Surfaces and applied them to a study case in North East Spain on winter crops adaptation, using rainfed winter wheat as reference crop.  Crop responses to perturbed temperature, precipitation and CO2 were simulated by an ensemble of crop models. A set of combined changes on cultivars (on vernalisation requirements and phenology) and management (on sowing date and irrigation) were considered as adaptation options and simulated by the crop model ensemble. The discussion focused on two main issues: 1) the recommended adaptation options for different soil types and perturbation levels, and 2) the need of applying our current knowledge (AOCK) when building a crop model ensemble. The study has been published Agricultural Systems (Available online 25 January 2017, https://doi.org/10.1016/j.agsy.2017.01.009 ), and the  text below consists on extracts from that paper.