Year 1 – 2014/2015:
1. A soybean phenology prediction web-tool (http://www.agron.iastate.edu/CroppingSystemsTools/ )
2. Preliminary analysis of the soybean yield gaps in Iowa
3. A list of research priorities for soybean based on model analysis and literature review
4. Results on N-fixation from the experimental trials (1st experimental season)
Year 2 – 2015/2016:
1. A calibrated version of the APSIM-soybean model that will drive further analysis
2. Updated version of the soybean yield gaps and factors causing these gaps
3. New results on N-fixation and the associated N credit (2nd experimental season).
4. Analysis of N uptake and N partitioning and NUE in soybeans
5. In-season soybean yield predictions in representative sites across Iowa
6. Scenario analyses to identify practices that increase profits and decrease N loss from the systems
Year 3 – 2016/2017:
1. Final version of the APSIM soybean model for scenario analysis and yield forecast
2. Soybean N credit and biological N fixation measurements finalized
3. N-rate and planting date effects on soybean yields, N uptake and distribution and yield components (pods/plant, seeds/pod) are finalized
4. Final version of the pre-season variety and planting date web-tool
5. In-season soybean yield predictions in representative sites across Iowa continued
6. Scenario analyses to identify practices that increase profits and decrease N loss from the systems
7. A list of management practices that will lead to reducing the yield gap
8. N budgets in representative soybean systems across Iowa
The results of this project will be presented at the Annual ISA Research Conference (Feb 2016 and Feb 2017), American Agronomy Society meetings, and will be disseminated also via On-Farm Advance newsletters, and ICM newsletters and scientific journal articles.
The results of our project will directly benefit ISA, Iowa soybean growers by:
a) Having a precise estimation of the soybean N fixation, and associated N credits for other crops and N fixation dynamics in different fields and environmental conditions. This information is very important for soil quality assessments, nutrient management for common cropping systems in Iowa and energy or life cycle analyses of soybean production.
b) Timely dissemination of the results: during the growing season when it is most needed, through our yield forecast project. Farmers can judge predictions and adapt their management as needed to ensure high yield levels. To our knowledge this is the first attempt to forecast soybean yields ahead of time in Iowa.
c) Accessing pre-season easy-to-use model based tools to assist decision making. We already developed the Soybean Planting Decision Tool where growers can quickly and at no cost perform scenario analysis of yield response to maturity and planting date at 9 locations in Iowa. Our goal is to develop more tools through this project
d) Understanding maximum soybean yield levels in Iowa and factors causing limitations and how the yield gap (difference between potential and actual) varies with management practices, soil conditions and climate variability. We already provided preliminary results for central Iowa and through the project we will expand this analysis to nine Iowa crop reporting districts.