I serve as the Regrow Ag lead for the Bill & Melinda Gates Foundation-sponsored "Niche" project - a $5M, 4 year grant which will bring together several key agriculture and climate tech partners to build an assessment framework and digital tools. Niche aims to optimize crop variety placement in Sub-Saharan Africa, led by Regrow Ag with NASA Harvest, One Acre Fund, and the University of Nebraska - Lincoln as key implementation partners. The project is intended to provide breeders, seed companies and advisories with information to optimize seed development and placement in Sub-Saharan African countries. This will allow users to develop climatically adaptive seed varieties more precisely, and rapidly, thereby improving crop resilience in the face of changing weather conditions and other stressors related to climate change.
I apply data analytics, statistics and programming to understand the crop ecophysiology behind crop models and remote sensing algorithms to improve crop production and reduce environmental impacts. My interests are in quantifying the effects of crop management, genetics and climate variability on soil-crop processes and integrating data into mechanistic models at different spatio-temporal scales. I have a passionate interest in disentangling sources of uncertainties/variability to better predict crop growth, both yield and quality and environmental indicators. This requires a broad understanding of how the environment and genetics influences crop growth/development; how rainfall, irrigation, and fertilizer influence soil conditions; how crops obtain water/nutrients from the soil; how soil processes contribute to the loss of C, N; and how all these processes interact.
My current role at Regrow Ag is focus on developing digital solutions by linking geospatial data (crop, management, environment and genetics), models, sensors, and scientific knowledge to make decision support tools more efficient and world-wide applicable. I work with data scientists, software developers and engineers to develop models/algorithms linking remote sensing data with crop/soil mechanistic models to predict crop phenology/yield, soil water, N and C dynamics in a broad range of agricultural systems (focus on maize, wheat). I work with multi-cultural and multi-disciplinary teams (Academia and Industry) and have experience working in 6 countries (Argentina, Australia, New Zealand, United States, The Netherlands and Germany).
Visit www.jojeda.com to learn more about my background and my current work.