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Universidade de Tras os Montes E Alto Douro (2019)

Efficient tools to simulate main crops in Portugal for decision support systems

Chenyao Yang

Titre : Efficient tools to simulate main crops in Portugal for decision support systems

Auteur : Chenyao Yang

Université de soutenance : Universidade de Tras os Montes E Alto Douro

Grade : PhD Thesis Agricultural Production Chains - From Fork to Farm 2019

Résumé partiel
Agricultural systems are inherently vulnerable to climate variability and climate change is expected to increase this vulnerability. Various studies warn the anthropogenic-driven global warming with elevated CO2 concentration and altered regional precipitation pattern, are expected to negatively affect local crop productivity and thus exacerbate food insecurities in many regions worldwide, particularly for Mediterranean basin. Mediterranean basin is one of the most prominent climate change “hotspot” due to ongoing and projected changes in both climate means and variabilities, comprising a robust climate change signal of an overall warming and drying trend, accompanied by more frequent occurrence of severe drought and extreme high temperatures. Specifically, these projected changes are expected to be more pronounced in southern Europe, such as in Portugal, where annual mean temperature has increased at a rate more than double the global warming rate in the past decades, along with the observed decreases in precipitation and its enhanced inter-annual variability. Therefore, it is urgently needed to carry out the assessment of climate change impacts on agricultural production and explore suitable adaptation strategies, whereas the related studies so far remain scarce in Portugal. We had chosen three important cropping systems for Portuguese agriculture, i.e. irrigated maize, rainfed wheat and perennial forage grassland, while representative study sites in their current principal growing regions were identified accordingly. The overall methodology follows combined use of climate and crop models, where the spatially-downscaled bias-corrected climate change projections from climate models were utilized to drive crop model simulations at study sites, which were prior calibrated using local observed weather, soil and management data. For employed process-based crop models, both STICS and AquaCrop were applied for the irrigated maize production, whereas the other two cropping systems were only analyzed using STICS model. It was noteworthy one major strength from current studies consisted in, on top of projected mean climate changes, we had consistently incorporated the effects of potential changes in climate variability and its associated extreme weather events into the simulated impacts (e.g. yield changes) for a more reliable assessment. The results indicate threats and risks of future climate change are substantially high for agriculture production in Portugal.


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