Dr. Sotirios Archontoulis

FACTS is an ongoing project developed to forecast and evaluate real-time soil-crop dynamics in specific ISU fields. Predictions and measurements will be frequently updated as new information becomes available during the growing season. 

What we do:

• During the growing season we provide real-time measurements and forecasts for weather, soil water and nitrogen, crop water and nitrogen, yield predictions, crop stage and heat/frost stress.
• Before/after the growing season we benchmark production, economic, and environmental performance, estimate the yield gaps, and perform what-if scenario analysis to identify management practices with the highest profits and lowest environmental impacts.

Why we do it:

• To provide quantitative answers to questions that farmers commonly ask such as what is going to be the yield this year, how much nitrogen is in the soil today, do I have enough soil water for the next few days, what if I had used more nitrogen, planted more seeds, gotten more rain.
• To improve the science behind predictive tools, ground-truth predictions, and explore different approaches to accurately forecasting yields.

Visit the project website for more information.

The overall goal of this project is to identify management practices to increase soybean yield potential and close gaps in Iowa. One of the outputs of this project is the Soybean Planting Decision tool.

​ The quality and moisture content of the seeds at harvest can significantly influence profitability. We collect corn and soybean seeds from field experiments to develop models to improve our ability to predict moisture content at harvest for new corn hybrids and soybean varieties.

Our civilization depends on continuously increasing levels of agricultural productivity, which itself depends on (among other things) the interplay of crop varieties and the environments in which these varieties are grown. Hence, to increase agricultural productivity and yield stability, it is necessary to develop improved crop varieties that deliver ever more yield, even under the variable weather conditions induced by global climate change, all the while minimizing the use of inputs such as fertilizers that are limiting, expensive or have undesirable ecological impacts. By coupling a network of innovative, low-cost nitrate sensors across multiple environments within the heart of the corn belt and advanced cropping systems modeling (APSIM, the most widely used modeling platform), the proposed research will enhance our understanding of and ability to predict yield and Genotype x Environment interactions. The integration of nitrate (N) dynamics into this model is expected to greatly increase the accuracy of its predictions. Because we will also integrate genotypes into this model, the proposed research outlines a new and innovative approach for breeding crops that exhibit increased yields and yield stability. It will be possible to readily translate this approach to other crops. By generating data on nitrate concentrations in soil and in planta at unprecedented spatial and temporal resolution at multiple sites with different soil characteristics and weather, the proposed research will also improve our understanding of N cycles in both the soil and plant. Although essential to plant growth and high yields, when over-applied N can result in a variety of serious negative externalities, some of which are currently the subject of high-impact litigation in Iowa. Project outcomes have the potential to provide guidance to farmers about how to apply sufficient but not excessive amounts of N fertilizer, resulting in both economic benefits to farmers and positive environmental externalities.Our focus on creating a new approach to breeding for yield stability meets the USDA sustainability goals to "satisfy human food and fiber needs" and "sustain the economic viability of farm operations". Our focus on nitrogen meets the USDA sustainability goals to "enhance environmental quality" and to "make the most efficient use of nonrenewable resources...and integrate, where appropriate, natural biological cycles and controls". More specifically, this proposal addresses the NIFA-Commodity Board co-funded priority for "development and application of tools to predict phenotype from genotype" and the "the development of high-throughput phenotyping equipment and methods".