LandPKS Modeling for Agricultural Sustainability: Meet our Modelers

We are lucky at LandPKS to have a wonderful team of people who work on various aspects of LandPKS.  Behind the scenes, we have two dedicated modelers who are working to take LandPKS input and model future agricultural scenarios to help LandPKS users make more sustainable land management decisions.  While these types of outputs are not currently available on LandPKS, we hope that in the near future such modeled future scenarios will be delivered to LandPKS users on their phones.

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Dr. Won Seok Jang is a hydrologist and modeler for LandPKS based at the University of Colorado Boulder, in the Sustainability Innovation Lab at Colorado (SILC).  Won Seok’s primary roles include developing a modeling framework and assessing the effect of soil degradation on crop productivity and local/regional/global scale hydrological modeling for climate change impact assessment.  He currently uses EPIC (Environmental Policy Integrated Climate) model to estimate potential crop yield and soil erosion and is developing an EPIC parallel computing framework for global modeling with big data.  The goal of his research is to explore and measure the impact of climate change on food production worldwide, to develop multi-objective optimization of crop yield and minimization of soil erosion initially in Eastern Africa with a plan to expand to the United States and other global locations in the future.

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Dr. Tegenu Engda is a postdoctoral researcher, also located at the University of Colorado Boulder, in SILC. His primary role is to understand Soil-Plant-Water interactions under different environmental conditions. He mainly uses the EPIC model to investigate factors affecting crop yield trends including hydrologic processes, erosion and management practices in the case of East Africa. Currently, he is working on sensitivity analysis of EPIC input soil properties to better understand yield estimates across soil types.  Tegenu is also working on EPIC model calibration and nutrient dynamics exploration, as well as incorporation of local knowledge to improve EPIC yield calculations.