The MINT project team will address primarily food production under changing scenarios, changes in the water cycle due to external forcing such as climate or market changes, and internal changes such as changes in food and water demand due to land use change including urban growth and purchasing power. Food production will be predicted based on process-based models (model Cycles) that are able to use daily or sub-daily climate information, and machine learning (Random Forest). The Cycles model is an evolution of C-Farm  and shares many modules with CropSyst . The Cycles model simulates yield and environmental impacts of food production based on fundamental biophysical principles that control plant growth, water and nutrient cycling. Its modular structure, transparent input and output structure, documentation in Github, and power to model any crop and crop rotation make the Cycles model an ideal component for integration in a modeling workflow alongside other models. Unlike process based models, machine learning algorithms can predict single indicators faster and using multiple sources of information, regardless of the specific mechanistic connection between predictor and predicted variable. They can be particularly powerful to predict crop production .
Dr. Kemanian has background in agroecology, systems modeling, and several disciplines central to agricultural and natural systems. He developed the Cycles model, components of the CropSyst model, and made contributions to the SWAT model and associated models EPIC and APEX. He is currently using the PIHM and Hydroterre platform, migrating to fully distributed models to represent terrestrial and aquatic processes. These models have been used in numerous projects of local, national and international reach.
- Kemanian, A.R. and Stöckle, C.O., 2010. C-Farm: A simple model to evaluate the carbon balance of soil profiles. European Journal of Agronomy, 32(1), pp.22-29.
- Stöckle, C.O., Kemanian, A.R., Nelson, R.L., Adam, J.C., Sommer, R. and Carlson, B., 2014. CropSyst model evolution: From field to regional to global scales and from research to decision support systems. Environmental Modelling & Software, 62, pp.361-369.
- Hoffman, A.L., Kemanian, A.R. and Forest, C.E., 2018. Analysis of climate signals in the crop yield record of sub‐Saharan Africa. Global Change Biology, 24(1), pp.143-157.