In the near-term future, population is expected to rise, with a 35% increase by 2050, requiring 50% more food due to changing diets 1. Coincident with this increase in population, climate is also changing — with potentially drastic impacts on food production. A large body of previous work has projected decreases in crop yield under this changing climate — largely due to increasing temperatures 2–5. Most studies, however, have neglected the role of vapor pressure deficit (VPD). A key knowledge gap remains for how crops will respond to rising temperatures versus increasing VPD levels, and the independent mechanisms through which the two climate factors can affect yield. A quantitative understanding of these climate impacts is critical foundational knowledge for selecting optimal plant traits and identifying management practices that can aid adaptation efforts within our agricultural systems under a changing climate.
The overarching goal of this project is to systematically quantify the vulnerability of future food security to changes in climate and identify region-specific mitigation and adaptation strategies. I will use a process-based maize model to address this goal with three main objectives: 1) Identify region-specific optimal plant traits and management practices that lead to maximum yield under current climate conditions. 2) Quantify the independent and spatially varying effects that temperature and VPD have on maize yields in the US, and gain a mechanistic understanding of the physiological processes that lead to their impacts on yield. 3) Identify regions that are vulnerable to yield loss under future climate conditions, and investigate the potential impact of mitigation and adaptation through cultivar selection and changes in management practices.
Maizsim is a process-based crop simualtion model developed and calibrated for maize plants. Them model represents key physiological and physical processes that occur within the life cycle of a maize plant such as gas exchange, canopy radiative transfer, carbon partitioning, water relations, nitrogen dynamics and phenology. The model is linked to a soil layer model (2DSOIL) that represents a dynamic soil, water, and nutrient vertical profile and has been tested and validated for a number of maize growing regions in the US and across the globe.
- init_files: initial files for maizsim
- notebooks: where all jupyter notebooks are stored
- raw_data_process:
- weadata:
- runs: model simualtion outputs