Tue, Dec 18, 2018
8:00am to 8:50am
Tue, Dec 18, 2018
1:00pm to 1:50pm
Prediction of the optimum N-rate for corn is complex as it involves weather, genetics, soils and management factors. Many crop-based and/or soil-based tools exist to provide N rate predictions that range from empirical (easy to use and understand) to mechanistic (complex to use and understand). Although there is no consensus on which tool is the best, there is a clear consensus that we need to further improve the accuracy of the tools. Mechanistic simulation models have the potential to provide scenarios in addition to N-rate predictions but these models are currently viewed as “black boxes”. In this presentation, I will open the “black box” and I will describe the science behind it and provide examples of pre-plant and in-season predictions of optimum N rates. Prediction accuracy, pros and cons of using modeling, and needs for improved predictions will be discussed. Finally, a new approach to predict N-rates for corn that is based on statistical modeling will be presented.
Sotirios Archontoulis is an assistant professor of integrated cropping systems at Iowa State University, Department of Agronomy. He completed his Ph.D. and MS in crop science and crop modeling at Wageningen University, the Netherlands and his postdoc research in cropping systems modeling at Iowa State University. Archontoulis’s research combines field-lab experimentation with simulation process-based models with the goal to improve production and environmental performance of various cropping systems with particular emphasis on corn and soybean crops growing in the US Midwest. His current research focus on identifying land use and management practices that can increase the efficiency of the system by means of increasing crop yields and simultaneously decreasing inputs or losses from the system. He is interested in deeper understanding the complex Genotype x Management x Environment interactions, adding mechanisms into models, and develop web-tools to disseminate research results in real-time to stakeholders.